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Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=hmep20 Media Psychology ISSN: 1521-3269 (Print) 1532-785X (Online) Journal homepage: https://www.tandfonline.com/loi/hmep20 Everyday functioning-related cognitive correlates of media multitasking: a mini meta-analysis Wisnu Wiradhany & Janneke Koerts To cite this article: Wisnu Wiradhany & Janneke Koerts (2019): Everyday functioning-related cognitive correlates of media multitasking: a mini meta-analysis, Media Psychology, DOI: 10.1080/15213269.2019.1685393 To link to this article: https://doi.org/10.1080/15213269.2019.1685393 © 2019 The Author(s). Published with license by Taylor & Francis Group, LLC. Published online: 03 Nov 2019. Submit your article to this journal Article views: 328 View related articles View Crossmark data https://www.tandfonline.com/action/journalInformation?journalCode=hmep20 https://www.tandfonline.com/loi/hmep20 https://www.tandfonline.com/action/showCitFormats?doi=10.1080/15213269.2019.1685393 https://doi.org/10.1080/15213269.2019.1685393 https://www.tandfonline.com/action/authorSubmission?journalCode=hmep20&show=instructions https://www.tandfonline.com/action/authorSubmission?journalCode=hmep20&show=instructions https://www.tandfonline.com/doi/mlt/10.1080/15213269.2019.1685393 https://www.tandfonline.com/doi/mlt/10.1080/15213269.2019.1685393 http://crossmark.crossref.org/dialog/?doi=10.1080/15213269.2019.1685393&domain=pdf&date_stamp=2019-11-03 http://crossmark.crossref.org/dialog/?doi=10.1080/15213269.2019.1685393&domain=pdf&date_stamp=2019-11-03 Everyday functioning-related cognitive correlates of media multitasking: a mini meta-analysis Wisnu Wiradhany a and Janneke Koerts b aDepartment of Experimental Psychology, University of Groningen, Groningen, the Netherlands; bDepartment of Clinical and Developmental Neuropsychology, University of Groningen, Groningen, the Netherlands ABSTRACT A recent meta-analysis has shown that media multitasking beha- vior, or consuming multiple streams of media simultaneously, might not be associated with less efficient cognitive processing, as measured with objective tests. Nevertheless, a growing num- ber of studies have reported that media multitasking is corre- lated with cognitive functioning in everyday situations, as measured in self-reports. Here, in a series of mini meta- analyses, we show that the self-reported correlates of media multitasking can be categorized in at least four major themes. Heavy media multitasking was associated with increasing pro- blems with attention regulation (e.g., increased mind-wandering and distractibility), behavior regulation (e.g., emotion regulation and self-monitor), inhibition/impulsiveness (e.g., higher level of impulsiveness and lower level of inhibition), and memory. However, the pooled effect sizes were small (z =.16 to z = .22), indicating that a large proportion of variance of media multi- tasking behavior is still unaccounted for. Additionally, we wit- nessed a high level of heterogeneity in the attention regulation theme, which might indicate the presence of the risk of study bias. In recent years, the number of studies investigating the correlates of habitual media multitasking behavior, i.e., consuming multiple streams of media-related information simultaneously, have increased. These studies investigate correlates of media multitaskers using both performance-based and self-reported measures, and have presented an interesting contradic- tion. On the one hand, the group of studies using performance-based measures, that is, highly controlled psychophysics experiments with clear instructions (e.g., to perform as quickly and as accurately as possible) and clear beginning and end, has shown mixed results. Specifically, some studies showed that Heavy Media Multitaskers (HMMs), compared to Light Media Multitaskers (LMMs) displayed worse performance in differ- ent objective, performance-based measures of cognition (Cain, Leonard, CONTACT Wisnu Wiradhany [email protected] Department of Experimental Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, the Netherlands MEDIA PSYCHOLOGY https://doi.org/10.1080/15213269.2019.1685393 © 2019 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. http://orcid.org/0000-0001-8707-3146 http://orcid.org/0000-0002-2317-0171 https://crossmark.crossref.org/dialog/?doi=10.1080/15213269.2019.1685393&domain=pdf&date_stamp=2019-11-02 Gabrieli, & Finn, 2016; Ophir, Nass, & Wagner, 2009; Ralph & Smilek, 2016), while others reported that HMMs performed better than LMMs (Alzahabi & Becker, 2013; Baumgartner, Weeda, van der Heijden, & Huizinga, 2014). Yet, others reported mixed findings and/or null results (Cardoso-Leite et al., 2015; Gorman & Green, 2016; Minear, Brasher, McCurdy, Lewis, & Younggren, 2013; Murphy, McLauchlan, & Lee, 2017; Ralph, Thomson, Seli, Carriere, & Smilek, 2015; Wiradhany & Nieuwenstein, 2017). With this mixed evidence, it is not surprising that a recent review (van der Schuur, Baumgartner, Sumter, & Valkenburg, 2015) and a meta-analysis (Wiradhany & Nieuwenstein, 2017) have shown that pooled together, the association between media multitasking and performances on performance-based measures of cognition is weak. Furthermore, the meta-analysis has shown that upon applying meta- analytic correction, the pooled association between media multitasking and performances on performance-based measures of cognition turned out to be null. On the other hand, there have been a growing number of studies showing associations between frequent media multitasking and problems reported on rating scales of cognition. Specifically, frequent media multitasking has been associated with more self-reported attention lapses and mind-wandering (Ralph, Thomson, Cheyne, & Smilek, 2013), higher levels of impulsiveness (Cain et al., 2016; Magen, 2017; Minear et al., 2013; Sanbonmatsu, Strayer, Medeiros-Ward, & Watson, 2013; Schutten, Stokes, & Arnell, 2017; Uncapher, Thieu, &Wagner, 2016), a higher number of problems with executive functions (Baumgartner et al., 2014; Magen, 2017), and more (severe) symptoms of Attention Deficit/Hyperactivity Disorders or ADHD (Magen, 2017; Uncapher et al., 2016). Together, these findings suggest that media multitasking is not associated with performances on objective measures of cognition, but never- theless, is associated with different aspects of everyday cognitive functioning. In cognition research, it is common to use both performance-based and self- reported methods for assessment as these two types of assessment often comple- ment one another (e.g., Chan, Shum, Toulopoulou, & Chen, 2008). At the same time, findings from both types of measurement might disagree with one another for several reasons. To start, the two measures arguably estimate one’s ability to function on different levels. Performance-basedmeasures estimate one’s optimal performance: These measures have explicit instructions and are administered under highly standardized conditions. Accordingly, the results of thesemeasures would reflect the efficiency of cognitive processing of an individual (Stanovich, 2009; Toplak, West, & Stanovich, 2013). In contrast, self-reported measures of the same construct estimate one’s typical performance: These measures probe a wide range of everyday behaviors which are related with the construct which is being estimated. Accordingly, the results of these measures would reflect the 2 W. WIRADHANY AND J. KOERTS ability of an individual to execute a task in conditions in which no explicit instructions or goals are given (Stanovich, 2009; Toplak et al., 2013). Critically, it is possible for an individual to score low in one type of measure but high in the other type and vice versa. In this light, the International Classification of Functioning, Disability, and Health (ICF), which is developed by the World Health Organization (World Health Organization, 2001), draws a distinction between functions (i.e., the struc- tural integrity of the body to allow for optimal use) and activities (i.e., the life areas, tasks, and actions associated with an individual). One can have an impairment on the activity level, but might perform well on the func- tional level. For instance, individuals with dysexecutive symptoms might report frequent problems in everyday situations, yet they perform relatively well in an executive function test (Burgess et al., 2006). Similarly, impair- ments on a functional level do not always necessarily result in impairments on the activity level due to compensation and adaptation. For instance, individuals with mild Alzheimer’s disease might perform poorly in an objective test, yet they are able to perform their daily activities using support from their environments (Farias, Harrell, Neumann, & Houtz, 2003). Accordingly, people who frequently media multitask might not perform worse in performance-based measures of cognition, yet report everyday problems associated with cognition due to the fact that laboratory measures might capture some, but not all aspects of cognition or that they measure cognition on a different level compared to self-reported measures. Correlates of media multitasking Due to the ubiquity of media devices in recent years (Lenhart, 2015; Marius & Anggoro, 2014), the frequency and duration of media multi- tasking behavior, consuming multiple streams of media information simul- taneously, have increased dramatically (Carrier, Cheever, Rosen, Benitez, & Chang, 2009; Rideout, Foehr, & Roberts, 2010; Roberts & Foehr, 2008). This behavior is mainly characterized by rapid switches of attention between different media streams. An observational study of concurrent television and computer usage showed that, on average, participants switched their attention 120 times within 27.5 min (Brasel & Gips, 2011). Similarly, another observational study reported that contemporary office workers spent on average 3 min on a task before switching to another (González & Mark, 2004). Switching does not only happen between media devices, but also between different media activities. For instance, Judd (2013) reported from computer session logs that college students switched between different tasks in a computer about 70% of the time and spent on average 2.3 min on one task before switching to another. MEDIA PSYCHOLOGY 3 With the high frequency of switching between different media streams, it is likely for media multitasking behavior to disrupt other ongoing cognitive and behavioral processes. With regard to cognitive processes, media multitasking might disrupt one’s current train of thoughts, which may result in worse task performance. In a study in which participants were asked to study an article about influenza, participants recalled less information about the article in con- ditions in which they were either forced to check their Facebook account or allowed to check their Facebook account while studying the article (Kononova, Joo, & Yuan, 2016). Other studies have shown that media-induced interruptions might have no significant impact on task performance (Fox, Rosen, & Crawford, 2009; Mark, Gudith, & Klocke, 2008), but nevertheless, people who experienced constant interruptions during work reported more stress and frustration at the end of the day (Mark et al., 2008). With regard to behavioral processes, media multitasking behavior might disrupt other everyday behavior patterns. For instance, adolescents who reported higher level of media multitasking also reported having fewer hours of sleep per night (Calamaro, Mason, & Ratcliffe, 2009). Similarly, in a longitudinal study, adolescents with a higher level of media multitasking reported more sleeping problems at the time of the data collection, 3 months, and 6 months later (van der Schuur, Baumgartner, Sumter, & Valkenburg, 2018). The current study Media multitasking behavior might interfere with different ongoing processes in everyday situations. This behavior might not be correlated with performances on objective measures of cognition (van der Schuur et al., 2015; Wiradhany & Nieuwenstein, 2017), but nevertheless, it might have profound impact on every- day cognitive functioning, as indicated by self-reported measures of cognition. This article aims to examine and summarize the current body of literature on media multitasking in order to create an overview of the different domains of everyday cognitive functioning which might be correlated with media multi- tasking behavior. The evidence was synthesized in a series of minimeta-analyses. Additionally, we also examined the risk of bias across the findings and per- formed a moderator analysis if risk of bias occurred. Methods Study selection All studies which investigated correlates of self-reportedmeasures of media multi- tasking and cognition were considered for inclusion. Studies were identified in the PsycInfo, ERIC, MEDLINE, SocINDEX, and CMMC databases, as well as the Directory of Open Access Journals (DOAJ) database. A combination of the 4 W. WIRADHANY AND J. KOERTS following keywords was entered in the search terms: media multitask* AND (problem* OR executive* OR impuls* OR attention*)1. Together, the search yielded 130 results from the first set of databases and 68 results from the DOAJ database. As Figure 1 shows, of the 198 studies identified, 40 were duplicates and therefore removed. Of the 158 studies, only 43 pertained to the term “media multitasking” (i.e., not only pertained to “media” or “multitasking” exclu- sively) and were therefore considered for further screening. Of 43 studies screened, we removed studies which did not meet the criteria below. First, studies must have examined the association between measures of media multitasking and self-report measures of cognition, or psychological traits or mental-health issues related to cognition. Therefore, four review articles (Aagaard, 2015; Carrier, Rosen, Cheever, & Lim, 2015; Lin, 2009; van der Schuur et al., 2015), two meta-analysis (Jeong & Hwang, 2016; Figure 1. A flow diagram showing the selection of study process. MEDIA PSYCHOLOGY 5 Wiradhany & Nieuwenstein, 2017), one measurement validity article (Baumgartner, Lemmens, Weeda, & Huizinga, 2017), 12 articles which only included laboratory task performance measures (Alzahabi & Becker, 2013; Alzahabi, Becker, & Hambrick, 2017; Cain & Mitroff, 2011; Edwards & Shin, 2017; Gorman & Green, 2016; Lui & Wong, 2012; Moisala et al., 2016; Murphy et al., 2017; Ophir et al., 2009; Ralph & Smilek, 2016; Ralph et al., 2015; Yap & Lim, 2013), two articles in which the level of media multitasking was manipulated (Kazakova, Cauberghe, Pandelaere, & De Pelsmacker, 2015; Lin, Robertson, & Lee, 2009), one article in which only a brain imaging measure was used (Loh & Kanai, 2014) and two articles in which only media multitasking behavior was observed (Loh, Tan, & Lim, 2016; Rigby, Brumby, Gould, & Cox, 2017) were excluded from further eligibility assessment. Second, since this study pertains to general media multitasking behavior (i.e., not a specific combination of two media), only studies using a general media multitasking measure were included. Therefore, one article in which only a specific combination of media multitasking was used (Kononova, Zasorina, Diveeva, Kokoeva, & Chelokyan, 2014) and one article (Wu, 2017) which measured the perception of media multitasking ability instead of actual media multitasking frequency were removed. Thirdly, we removed two articles that measured the association between media multitasking and well-being or constructs which are related to well-being (Hatchel, Negriff, & Subrahmanyam, 2018; Pea et al., 2012). Lastly, one article was excluded since the relevant effect sizes could not be extracted from the published article (Shih, 2013)2. In all, a total of 13 articles containing 15 independent studies3 were included for synthesis (Baumgartner, van der Schuur et al., 2017, 2014; Cain et al., 2016; Cardoso-Leite et al., 2015; Duff, Yoon, Wang, & Anghelcev, 2014; Hadlington & Murphy, 2018; Magen, 2017; Minear et al., 2013; Ralph et al., 2013; Sanbonmatsu et al., 2013; Schutten et al., 2017; Uncapher et al., 2016; Yang & Zhu, 2016). Table 1 shows the measures of self-reported functioning included in each study and the number of participants assessed. Effect size selection and calculation Effect sizes were selected from reported outcome measures which reflect distinguishable constructs. For instance, a study examining the association between media multitasking and measures of executive function would report measures of attentional shifting, working memory, and inhibition, which are separate constructs. Study findings related to these measures would be regarded as individual effect sizes. In total, 48 unique effect sizes were extracted from the studies listed in Table 1 and included in the final series of mini meta-analysis. Effect sizes were calculated in Fisher’s z, indicating the normalized correla- tion coefficients between self-reported measures of media multitasking and 6 W. WIRADHANY AND J. KOERTS Ta bl e 1. O ve rv ie w of in cl ud ed st ud ie s in m et a- an al ys is ,i nc lu di ng th e nu m be r of pa rt ic ip an ts ,a nd th e m ea su re s of se lf- re po rt ed fu nc tio ni ng us ed in ea ch st ud y. Au th or s (y ea r) N to ta l M ea su re (s ) of se lf- re po rt ed fu nc tio ni ng Sa m pl e de sc rip tio n Ba um ga rt ne r et al .( 20 14 ) 52 3 Be ha vi or Ra tin g In ve nt or y of Ex ec ut iv e Fu nc tio ns (B RI EF ): W or ki ng M em or y, In hi bi tio n, an d Sh ift in g su bs ca le s 48 % fe m al es ,M ag e = 13 .0 9 Ba um ga rt ne r, va n de r Sc hu ur et al .( 20 17 ,S tu dy 1, w av e 1) 12 41 In at te nt iv en es s sc al e- ba se d on D SM -V cr ite ria fo r AD H D 49 % fe m al es ,M ag e = 12 .6 1* Ba um ga rt ne r, va n de r Sc hu ur et al .( 20 17 ,S tu dy 1, w av e 2) 12 16 In at te nt iv en es s sc al e- ba se d on D SM -V cr ite ria fo r AD H D 49 % fe m al es ,M ag e = 12 .6 1* Ba um ga rt ne r, va n de r Sc hu ur et al .( 20 17 ,S tu dy 1, w av e 3) 11 03 In at te nt iv en es s sc al e- ba se d on D SM -V cr ite ria fo r AD H D 49 % fe m al es ,M ag e = 12 .6 1* Ba um ga rt ne r, va n de r Sc hu ur et al .( 20 17 ,S tu dy 2, w av e 1) 10 83 In at te nt iv en es s sc al e- ba se d on D SM -V cr ite ria fo r AD H D - Ba um ga rt ne r, va n de r Sc hu ur et al .( 20 17 ,S tu dy 2, w av e 2) 93 9 In at te nt iv en es s sc al e- ba se d on D SM -V cr ite ria fo r AD H D - Ba um ga rt ne r, va n de r Sc hu ur et al .( 20 17 ,S tu dy 2, w av e 3) 43 9 In at te nt iv en es s sc al e- ba se d on D SM -V cr ite ria fo r AD H D 59 % fe m al es ,M ag e = 14 .3 7 Ca in et al .( 20 16 ) 70 D om ai n- sp ec ifi c im pu ls iv ity in sc ho ol -a ge ch ild re n (D iS C) 49 .3 1% fe m al es ,M ag e = 14 .4 Ca rd os o- Le ite et al .( 20 15 ) 60 Co gn iti ve Fa ilu re Q ue st io nn ai re (C FQ ), At te nt io n D ef ic it/ H yp er ac tiv ity D is or de r Se lf- Re po rt Sc al e (A D H D -A SR S) 13 .3 3% fe m al es ,M ag e = 20 .6 8 D uf f et al .( 20 14 ,S tu dy ,p .1 ) 30 8 Co gn iti ve Fa ilu re s Q ue st io nn ai re (C FQ ), Pe rs on al Co nt ro lS ca le (P CS ), Br ie f Se ns at io n- se ek in g Sc al e (B -S SS ) 58 .1 2% fe m al es ,M ag e = 20 .3 7 D uf f et al .( 20 14 ,S tu dy ,p .2 ) 50 1 Co gn iti ve Fa ilu re s Q ue st io nn ai re (C FQ ), Pe rs on al Co nt ro lS ca le (P CS ), Br ie f Se ns at io n- se ek in g Sc al e (B -S SS ) 51 .0 9% fe m al es ,M ag e = 34 .4 3 H ad lin gt on an d M ur ph y (2 01 8) 14 4 Ri sk y Cy be rs ec ur ity Be ha vi or (R cS B) ,C og ni tiv e Fa ilu re Q ue st io nn ai re (C FQ ) 77 .7 7% fe m al es ,M ag e = 20 .6 3 M ag en (2 01 7) 19 6 Be ha vi or Ra tin g In ve nt or y of Ex ec ut iv e Fu nc tio ns (B RI EF ): al ls ub sc al es ,A tt en tio n D ef ic it/ H yp er ac tiv ity D is or de r Se lf- Re po rt Sc al e (A D H D -A SR S) 74 % fe m al es ,M ag e = 23 .4 4 M in ea r et al .( 20 13 ) 22 1 Ba rr at t Im pu ls iv en es s Sc al e (B IS ) 68 .3 2% fe m al es ,M ag e = 19 .8 Ra lp h et al .( 20 13 ) 20 2 M in df ul At te nt io n Aw ar en es s Sc al e – La ps es O nl y (M AA S- LO ), At te nt io n- re la te d Co gn iti ve Er ro rs Sc al e (A RC ES ), M em or y Fa ilu re s Sc al e (M FS ), M in d W an de rin g- Sp on ta ne ou s (M W -S ), M in d W an de rin g- D el ib er at e (M W -D ), At te nt io na lC on tr ol -S w itc hi ng (A C- S) ,A tt en tio na lC on tr ol - D is tr ac tib ili ty (A C- D ) 72 .2 8% fe m al es ,u nd er gr ad ua te st ud en ts Sa nb on m at su et al .( 20 13 ) 27 7 Ba rr at t Im pu ls iv en es s Sc al e (B IS ), Se ns at io n- se ek in g Sc al e (S SS ) 56 .7 7% fe m al es ,M ed ia n a g e = 21 Sc hu tt en et al .( 20 17 ) 30 3 Ba rr at t Im pu ls iv en es s Sc al e (B IS ) 83 .2 3% fe m al es ,M ag e = 19 .6 3 U nc ap he r et al .( 20 16 ) 13 9 Ba rr at t Im pu ls iv en es s Sc al e (B IS ), At te nt io n D ef ic it/ H yp er ac tiv ity D is or de r Se lf- Re po rt Sc al e (A D H D - AS RS ) 58 .0 4% fe m al es ,M ag e = 22 .1 Ya ng an d Zh u (2 01 6) 31 0 Ba rr at t Im pu ls iv en es s Sc al e (B IS ), Br ie f Se ns at io n- se ek in g Sc al e (B -S SS ) 49 .3 5% fe m al es ,M ag e = 15 .3 *T he se x pr op or tio n an d M ea n of ag e re fe rs to th e co m bi ne d sa m pl es of St ud y 1 ac ro ss th e th re e st ud y w av es . MEDIA PSYCHOLOGY 7 self-reported measures of cognition. A positive z indicates that frequent media multitasking is associated with more (severe) issues and a negative z indicates that frequent media multitasking is associated with less (severe) issues. In most cases, the included studies reported Pearson’s product- moment correlations (r) as measures of effect sizes. These r’s were converted into Fisher’s z using formula 1 below (Borenstein, Hedges, Higgins, & Rothstein, 2009): z ¼ 0:5� ln 1þ r 1� r � � (1) In which r is the Pearson’s product-moment correlation. Analysis Categorization of findings Since different studies featured in the meta-analysis and the featured rating scales measured different domains of cognition, we grouped the respective effect sizes into different categories based on the similarity and dissimilarity between constructs. To illustrate, the Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003) and the self-monitoring subscales of the Behavioral Ratings of Executive Functions (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000; Gioia, Isquith, Retzlaff, & Espy, 2002) infer a relatively similar construct related to attention regulation, which is relatively dissimilar to the construct related to forming precise information in memory inferred by the Memory Failures Scale (MFS; Carriere, Cheyne, & Smilek, 2008). To guide the categorization of our effect sizes, we first made a table of self- report measures in Table 1 along with the goal of each individual measure and some examples of its items. Considering the goal of the measure and its items, we then looked into the literature to gain insights on how to group them in a meaningful way. For instance, measures related to impulsiveness such as the Barratt Impulsiveness Scale (BIS; Patton, Stanford, & Barratt, 1995), measures related to inhibition such as the BRIEF-Inhibition (Gioia et al., 2002), and measures related to sensation seeking such as the Sensation- Seeking Scale (SSS; Zuckerman, 1996) were group together under the over- arching construct of impulsiveness/inhibition (e.g., Dalley, Everitt, & Robbins, 2011). Some measures, such as the ADHD-Adult Self Report Scale (ADHD-ASRS; Kessler et al., 2005) and the Cognitive Failure Questionnaire (CFQ; Broadbent & Cooper, 1982) might have two or more distinct underlying constructs; the former has attention and impulsiveness components and the latter has attention, memory, and other components. To ensure that our resulting categories were as independent from each other as they can be, in the case of the ADHD-ASRS, we contacted the authors to request additional data regarding the correlation between media 8 W. WIRADHANY AND J. KOERTS multitasking and attention deficit and between media multitasking and hyperactivity/impulsiveness separately. In the case of the CFQ, considering that studies, especially recent ones were in disagreement with regard to the underlying dimensions of CFQ (Bridger, Johnsen, & Brasher, 2013; Larson, Alderton, Neideffer, & Underhill, 2011; Wallace, Kass, & Stanny, 2002), we decided to categorize the effect sizes pertained to the general CFQ scores twice: once in the attention regulation category and once in the memory category. For all categories, the first author performed the categorizations and the second author checked the resulted categories. Disagreements between authors were resolved by consensus. Using the categorization processes above, we identified four different themes for correlates between media multitasking and self-reports of every- day cognitive functioning, namely attention regulation, behavior regulation, impulsiveness/inhibition, and memory. The attention regulation theme per- tained to the set of cognitive abilities which help to boost information processing. Traditionally, this includes the ability to react to important cues in the environment (alerting of attention), select relevant from irrelevant information (orienting of attention), and switch from one stimulus-response task rule to another (executive attention; Petersen & Posner, 2012; Posner & Petersen, 1990). More recently, our ability to suppress internally generated task-unrelated thoughts (Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016; Smallwood & Schooler, 2006) was also considered in this array of cognitive processing-boosting ability. Accordingly, in this theme, we included measures of attention orientation/selection (e.g., ADHD-ASRS – Inattention subscale), distractibility (e.g., CFQ – distractibility subscale and CFQ – Total), switching from one task to another (e.g., BRIEF – Shifting subscale), and mind-wandering (e.g., MAAS, Mind-Wandering scale). The impulsiveness/inhibition theme pertained to the ability to inhibit premature thoughts and actions, and difficulties with this ability can be exhibited behaviorally in one’s tendency to seek additional stimulations and take risks (Dalley et al., 2011; Dalley & Robbins, 2017). Here, we included measures which were related to inhibition (BRIEF-Inhibition), behavior impulsiveness (e.g., BIS, ADHD-ASRS – Hyperactivity/impulsivity subscale), and sensation-seeking (e.g., SSS). The memory theme pertained to the ability to retain information do mental work with information in memory (e.g., Diamond, 2013). This ability has been considered to be relatively independent to attentional regulation, yet it has been considered to play an important role in executive functioning (Diamond, 2013; Engle, 2002; Engle & Kane, 2004). In this meta-analysis, this theme included measures of working memory (e.g., BRIEF – Working memory subscale), memory failures (e.g., Memory Failures Scale), and gen- eral cognitive failures (e.g., CFQ – Total score). MEDIA PSYCHOLOGY 9 Lastly, the behavior regulation theme pertained to the set of abilities which is related to the volitional control of action. According to one taxonomy of executive function (BRIEF; Gioia et al., 2002; Huizinga & Smidts, 2010), behavior regulation is an umbrella term which includes task-switching and inhibition as well. However, as discussed above, task-switching appears to be more related to attention regulation (e.g., Petersen & Posner, 2012) while inhibition, which is more internally driven, appears to be more related to impulsiveness and risk-taking (e.g., Dalley et al., 2011). Accordingly, we categorized task-switching and inhibition in the attention regulation and impulsiveness/inhibition categories, respectively. Thus, what remains in our behavior regulation theme were abilities that relate to volitional control of action which are driven by external demands and situational factors (e.g., see Tsukayama, Duckworth, & Kim, 2013). This theme included measures of self-control (e.g., Domain-specific Impulsivity in School-age Children4; Personal Problem-solving Inventory – Self-control subscale), emotion regu- lation (e.g., BRIEF – Emotion regulation subscale), and self-monitoring (e.g., BRIEF – Self monitoring subscale). Note that while we sought out to minimize overlaps between the themes and categorize the findings as accurately as possible, the categorization remained somewhat arbitrary as different theoretical models of cognitive function would have both overlaps and distinctions of different sets of cognitive ability (e.g., see Chan et al., 2008). For each theme, a random-effect model and a pooled effect size were calculated to provide estimates of the magnitude of the correlation. Random-effect model Since the current meta-analysis featured different rating scales and outcome measures, we constructed a random-effect model to estimate the pooled effect size. This model assumes that the different scales had comparable, but not identical effect sizes which are distributed around some mean that reflected the true effect (Borenstein et al., 2009). In our case, we assumed that the different outcomes measured different subsets of cognitive functioning. Thus, the effects might vary from one function to another. The random-effect model was constructed in R (R Core team, 2015) using the metafor package (Viechtbauer, 2010). To account for variance inflation of the pooled effect size due to the dependency of multiple outcome measures from one study, we calculated the robust variance estimation (RVE; Hedges, Tipton, & Johnson, 2010). RVE works by estimating the correlations between dependent outcome measures and adjusting the standard error of the pooled effect size based on these correlations (Hedges et al., 2010; Scammacca, Roberts, & Stuebing, 2013). 10 W. WIRADHANY AND J. KOERTS Heterogeneity and risk of bias When significant between-studies heterogeneity was detected, we performed a moderator analysis and a risk of bias analysis. The moderator analysis assesses whether the between-studies heterogeneity can be explained by shared characteristics of different subgroups of studies (Hedges & Pigott, 2004). The risk of bias analysis tested whether the heterogeneity was stemming from bias coming from the level of precision in each study. Under a presence of bias, it is common for studies with smaller sample sizes to show an overestimation of effect sizes due to sampling errors compared with studies with bigger sample sizes, a phenomenon called small-study effect (Sterne, Gavaghan, & Egger, 2000). A small-study effect might indicate the presence of publication bias, since other studies with smaller sample sizes showing underestimation of the effect ended up not being published (Ioannidis, 2005; Ioannidis, Munafò, Fusar-Poli, Nosek, & David, 2014). As a formal inspec- tion of small-study effects, we conducted an Egger’s test (Egger, Davey Smith, Schneider, & Minder, 1997), in which a simple linear regression with effect sizes as a measure of magnitude of study effect and sample sizes or standard errors as measures of study precision is constructed. Results Attention regulation Random-effect model Figure 2 shows a forest plot for a group of self-report scales which measured the association between media multitasking and constructs related to the ability to regulate attention. The scales categorized in this theme included Attentional Control (AC)-switching (e.g., “I am slow to switch from one task to another,” Carriere, Seli, & Smilek, 2013), Attentional Control (AC)- distractibility (e.g., “I have difficulties concentrating when there is music in the room around me”, Carriere et al., 2013), ADHD-ASRS – Inattention (e.g., “How often do you have difficulty concentrating on what people are saying to you even when they are speaking to you directly?”, Kessler et al., 2005), MW – Spontaneous (e.g., “I find my thoughts wandering spontaneously”, Carriere et al., 2008), MW – Deliberate (e.g., “I allow my thoughts to wander on purpose”, Carriere et al., 2008), MAAS – Lapses only (e.g., “I do jobs or tasks automatically, without being aware of what I’m doing”, Brown & Ryan, 2003), ARCES (e.g.,“I have gone to the fridge to get one thing (e.g., milk) and taken something else (e.g., juice),” Carriere et al., 2008), BRIEF-Shift (e.g., “I get stuck on one topic or activity,” Gioia et al., 2002); CFQ – Distractibility, also with CFQ – total score (e.g., “Do you read something and find you MEDIA PSYCHOLOGY 11 haven’t been thinking about it and must read it again?,” Broadbent & Cooper, 1982). Overall, the pooled effect size of the correlates between media multitasking and self-reported problems related to attention regulation was small, yet statistically significant, z = .162, 95% CI [.160, .164], p < .001. At the same time, however, a significant heterogeneity between the effect sizes was detected, I2 = 86.76%, Q(21) = 118.93, p < .001. Heterogeneity & risk of bias analysis To address the heterogeneity in the model, we performed moderator analyses with two moderators. First, we added sex, as indicated by the proportion of females in the study samples as a moderator. Second, we added age, as indicated by the mean age of the study samples as a moderator. The two moderators did not contribute to the unexplained variance in the model, F(1, 17) = 2.59, p = .125; F(1, 11) = 3.08, p = .107, respectively, indicating that the heterogeneity could not be explained by differences in sex, and age. As for the risk of bias, the Egger’s test showed no relationship between effect size and study precision, z = −1.46, p = .144. This indicates that under the presence of heterogeneity, effect sizes were stable across different studies with different sample sizes. Figure 2. Forest plot of the effect sizes (Fisher’s z) for studies measuring the association between media multitasking and attention regulation. Error bars indicate 95% confidence intervals of the means. AC: Attentional Control scale; ADHD-ASRS: Attention Deficit/Hyperactivity Disorder Adult Self Report Scale; ARCES: Attention-Related Cognitive Error Scale; BRIEF: Behavior Rating Inventory of Executive Function; CFQ: Cognitive Failure Questionnaire; MAAS: Mindful Awareness Attention Scale; MW: Mind-Wandering scale. 12 W. WIRADHANY AND J. KOERTS Impulsiveness – inhibition Random-effect model Figure 3 shows a forest plot for a group of self-report scales which measured the association between media multitasking and constructs related to impul- siveness and/or inhibition. The scales categorized in this theme included ADHD-ASRS – hyperactivity (e.g., “How often do you fidget or squirm with your hands or your feet when you have to sit down for a long time?”, Kessler et al., 2005), BIS (e.g. “I do things without thinking”, Patton et al., 1995), BRIEF-Inhibit (e.g., “I do not think before doing,” Gioia et al., 2002), SSS, also the brief version; B-SSS (e.g., “I sometimes like to do things that are a little frightening”, Zuckerman, 1996), and RCsB (e.g., “Sharing passwords with friends and colleagues”). Overall, the pooled effect size of the correlates between media multitasking and self-reported problems related to impulsiveness and/or inhibition was small, yet statistically significant, z = .219, 95% CI [.218, .219], p < .001. The between-studies heterogeneity was low, I2 < .001%, Q(14) = 9.82, p = .775, indicating that the effect was consistent across different studies. Memory Random-effect model Figure 4 shows a forest plot for a group of self-report scales which measured the association between media multitasking and constructs related to mem- ory. The scales categorized in this theme included BRIEF-Working Memory Figure 3. Forest plot of the effect sizes (Fisher’s z) for studies measuring the association between media multitasking and impulsiveness and/or inhibition. Error bars indicate 95% confidence intervals of the means. ADHD-ASRS: Attention Deficit/Hyperactivity Disorder – Adult Self Report Scales; B-SSS: Brief Sensation-Seeking Scale; BIS: Barratt Impulsiveness Scale; BRIEF: Behavior Rating Inventory of Executive Function; RCsB: Risky Cybersecurity Behavior scale; SSS: Sensation- seeking Scale. MEDIA PSYCHOLOGY 13 (e.g., “I have trouble remembering things, even for a few minutes,” Gioia et al., 2002), CFQ (e.g., “Do you find you forget people’s names?”, Broadbent & Cooper, 1982), and MFS (e.g., “I forget what I went to the supermarket to buy”, Carriere et al., 2008). Overall, the pooled effect size of the correlates between media multitasking and self-reported problems related to memory was small, yet statistically significant, z = .158, 95% CI [.156, .161], p < .001. The between-studies heterogeneity was low, I2 = 16.49%, Q(4) = 4.67, p = .323, indicating that the effect was consistent across different studies. Behavior regulation Random-effect model Figure 5 shows a forest plot for a group of self-report scales which measured the association between media multitasking and constructs related to the ability to regulate behavior. The scales categorized in this theme included Emotional Control (e.g. “Has outburst for little reason”, Gioia et al., 2002), BRIEF-Initiate (e.g., “I need to be told to begin a task even when willing”), Figure 4. Forest plot of the effect sizes (Fisher’s z) for studies measuring the association between media multitasking and memory. Error bars indicate 95% confidence intervals of the means. BRIEF: Behavior Rating Inventory of Executive Function; CFQ: Cognitive Failures Questionnaire; MFS: Memory Failures Scale. Figure 5. Forest plot of the effect sizes (Fisher’s z) for studies measuring the association between media multitasking and behavior regulation. Error bars indicate 95% confidence intervals of the means. BRIEF: Behavior Rating Inventory of Executive Function; DISC: Domain-specific Impulsivity in School-age Children; PPSI: Personal Problem-solving Inventory. 14 W. WIRADHANY AND J. KOERTS BRIEF-Organization of Materials (e.g., “I cannot find things in room or school desk”), Plan/Organize, (e.g., “I become overwhelmed by large assign- ments”), BRIEF-Self-monitor (e.g. “I am unaware of how my behavior affects or bothers others”), BRIEF-Task-Monitor (e.g., “I make careless errors”), DiSC (e.g., “I interrupted other people” Tsukayama et al., 2013), and PPSI- Personal control (e.g., “Sometimes I do not stop and take time to deal with my problems, but just kind of muddle ahead” Heppner & Petersen, 1982). Overall, the pooled effect size of the correlates between media multitasking and self-reported problems related to behavior regulation was small, yet statistically significant, z = .192, 95% CI [.190, .193], p < .001. The between- studies heterogeneity was low, I2 = 6.67%, Q(8) = 5.69, p = .683, indicating that the effect was consistent across different studies. General discussion In this meta-analysis, we examined the correlates of media multitasking behavior with different domains of everyday cognitive functioning in a series of mini meta-analysis. The effect sizes were categorized into different themes reflecting different domains of everyday cognitive functioning, based on the similarities and dissimilarities between the reflected constructs. Overall, the effect sizes can be categorized into four distinct themes. Pooled together, frequent media multitasking had weak, but significant associations with a decrease of attention regulation (z = .16), lower levels of inhibition/higher levels of impulsiveness (z = .22), an increase of memory problems (z = .16), and a decreased behavior regulation (z = .19). Regarding the association between media multitasking and attention reg- ulation, we found that heavy media multitasking was associated with higher frequency of mind-wandering, higher distractibility, and more problems with task switching. With regard to mind-wandering, this finding was somewhat consistent with other findings in the literature which used objective mea- sures. In an experiment in which participants were asked to memorize materials from a video-recorded lecture, Loh et al. (2016) found that heavy media multitaskers retained less information from the lecture, and this effect could be explained by the increased tendency to mind-wander in this group. However, at least one study showed a null correlation between media multi- tasking and mind-wandering: heavy media multitaskers performed worse in a metronome-response task, but they did not show a tendency to have increased mind-wandering during the experiment (Ralph et al., 2015). With regard to distractibility, other studies which used objective measures showed that, for instance, heavy media multitaskers were less able to filter out irrelevant information from their immediate environment (Cain & Mitroff, 2011; Ophir et al., 2009). However, a recent meta-analysis (Wiradhany & Nieuwenstein, 2017) has shown that other studies have failed to replicate this MEDIA PSYCHOLOGY 15 finding. Lastly, with regard to task-switching, other studies using objective tests showed mixed evidence for the correlation between media multitasking and task switching. Some studies found a negative correlation between media multitasking and task performance (Ophir et al., 2009; Wiradhany & Nieuwenstein, 2017, Exp., p. 1), others found positive correlations (Alzahabi & Becker, 2013), yet others found null results (see Wiradhany & Nieuwenstein, 2017, for a meta-analysis). Thus, it can be said that while frequent media multitasking is associated with more problems with atten- tional control in everyday situations, media multitasking might not directly influence one’s ability to regulate attention, as measured by objective tests, per se. Heavier media multitasking was associated with increased impulsiveness/ decreased inhibition; heavier media multitaskers were associated with higher scores in impulsiveness traits and they reported more (severe) symptoms of hyperactivity/impulsivity. Heavier media multitasking was also associated with higher scores in other traits which are related to impulsiveness, such as sensation-seeking and risk-taking (Dalley et al., 2011; Whiteside & Lynam, 2001). This finding was consistent with findings using objective measures. For instance, heavy media multitaskers were more likely to choose smaller, immediate rewards instead of later, larger ones and they endorsed intuitive, but incorrect answers of the Cognitive Reflection Test (Schutten et al., 2017). Additionally, another study indicated that HMMs scored lower in a fluid intelligence test due to them giving up earlier in the test (Minear et al., 2013). Individuals with higher levels of sensation-seeking trait are characterized by a higher stimulation threshold for optimal behavioral performance (Hoyle, Stephenson, Palmgreen, Lorch, & Donohew, 2002; Zuckerman, 2007) and a higher likelihood to act prematurely without foresight, which at times lead to risk-taking behaviors (Dalley et al., 2011; Hoyle et al., 2002; Zuckerman, 2007). Indeed, consuming multiple streams of information has been shown to promote a higher level of engagement (Bardhi, Rohm, & Sultan, 2010; Wang & Tchernev, 2009) and to provide gratifications (Hwang, Kim, & Jeong, 2014) which together provide stimulations for those who seek them. Accordingly, people with higher levels of sensation-seeking and risk-taking might media multitask to seek for additional stimulations. Heavier media multitasking was associated with increased problems related to memory. In this regard, one study using an objective measure, namely a change-detection task has shown that HMMs had difficulties retaining specific information in working memory, regardless of the presence of distractors, and importantly, they performed more poorly in a later long- term memory test for both relevant and irrelevant objects compared to LMMs (Uncapher et al., 2016; but see Wiradhany, van Vugt, & Nieuwenstein, 2019 for null results). Other studies have shown that HMMs, compared to LMMs performed worse in a complex working memory 16 W. WIRADHANY AND J. KOERTS test (Sanbonmatsu et al., 2013) and an N-back test (Cain et al., 2016; Ophir et al., 2009; Ralph & Smilek, 2016; but see Cardoso-Leite et al., 2015; Wiradhany & Nieuwenstein, 2017 for null results). Lastly, regarding the association between media multitasking and behavior regulation, we found that heavier media multitaskers had more difficulties to adjust their thoughts, emotions, and actions to the situational demands. This finding is in line with its counterpart in objective tasks, where studies have found that HMMs performed worse in tasks in which they have to respond to different cue-probe contingencies, such as the AXE-CPT task (Ophir et al., 2009; Wiradhany & Nieuwenstein, 2017; but see Cardoso-Leite et al., 2015 for null results). However, there were mixed findings with regard to perfor- mance of HMMs in a change-detection task in which distractor filtering was involved, with one study showed that HMMs performed worse (Ophir et al., 2009), while other, recent ones showed null findings (Cardoso-Leite et al., 2015; Uncapher et al., 2016; Wiradhany & Nieuwenstein, 2017; Wiradhany et al., 2019). Collectively, we witnessed a discrepancy between the findings in this meta- analysis and the findings in a previous meta-analysis (Wiradhany & Nieuwenstein, 2017). In this meta-analysis, we found overall weak, but stable- pooled correlations between media multitasking and self-reports of cognitive functioning in everyday situations whereas in the previous meta-analysis, we found an overall weak-pooled correlation, but the correlation became null upon corrections. This discrepancy, as we previously mentioned, might exist for several reasons. First, performance-based measures might capture some, but not all aspects of everyday cognitive functioning. Consider the tests for one’s ability to regulate attention, for instance. One group of researcher may assess attention regulation using the perspective of mind-wandering to inves- tigate the waxing and waning of attention (e.g., Christoff et al., 2016). Yet, others assess attention regulation using the perspective of divided attention (e.g., Moisala et al., 2016). The two perspectives might cover some, but not all aspects of attention regulation, and in everyday situations one might need to suppress both mind-wandering and distraction to regulate attention properly. Second, performance-based measures are often designed to assess one’s ability at a pathological level (Chan et al., 2008). For instance, as a diagnostic tool to inquire whether one’s ability to regulate attention is clearly impaired. Therefore, one interpretation of the weak correlations across all themes would be that media multitasking behavior is associated with the increased number of everyday problems related to cognition, but this does not mean that media multitasking is associated with the presence of an impairment in cognitive abilities. The weak correlations between media multi- tasking and everyday cognitive functioning as shown in self-reports suggest that performance-based measures might not be adequately sensitive to detect everyday cognitive problems in media multitaskers. MEDIA PSYCHOLOGY 17 Notes on causality Media multitasking behavior might precede, occur as a consequence, or have a reciprocal relationship with everyday cognitive functioning. Currently, this meta-analysis does not allow for disentangling the causal relationship between the two. Preceding problems with cognition, media multitasking behavior may promote a specific mode of processing information in the environment (Lin, 2009. Specifically, heavy media multitaskers might develop a breadth-biased focus of attention, due to constant exposures to media- saturated environments. That is, they prefer to skim a large quantity of information rather than deeply processing a small amount of information. Consequently, adopting this mode of information processing might lead media multitaskers to apply cognitive control processes such as thought- monitoring and attention regulation less strictly. This might have profound consequences. In an fMRI study; Moisala et al. (2016) found that in addition to worse task performance in which participants had to attend to sentences in one modality (e.g. auditory) while they had to ignore distractor sentences presented in another modality (e.g. visual), HMMs, compared to LMMs also have higher activations in the right superior and medial frontal gyri, and the medial frontal gyrus. Increased activations in these areas have been linked to, among others, increased top-down attentional control. Therefore, heavy media multitaskers might require more effort in filtering distracting informa- tion than light media multitaskers. Alternatively, it could also be the case that media multitasking behavior leads to overreliance of exogenous control of attention (i.e. from incoming notifications from media; Ralph et al., 2013). Consequently, heavy media multitaskers train their endogenous control less often and thus, experience more problems related to cognitive control. Media multitasking behavior might also occur as a consequence of existing problems with cognition. People with ADHD and people with problems with behavior regulation and metacognition are more easily distracted and therefore have a higher propensity to media multitask. Similarly, people with high levels of sensation-seeking are more inclined to media multitask for stimulation- seeking purposes. Relatedly, indicating that excessive media multitasking beha- vior might be a result from a preexisting condition, studies have also shown that individuals with smaller grey matter volumes in the Anterior Cingulate Cortex (ACC) – a brain region which has been shown to be more active during error and conflict detections (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Botvinick, Cohen, & Carter, 2004) – reported higher levels of media multi- tasking (Loh & Kanai, 2014). Similarly, the increased activations of the brain areas associated with top-down control in heavy media multitaskers (Moisala et al., 2016) might also indicate that these areas function less efficiently in heavy media multitaskers, compared to light media multitaskers. 18 W. WIRADHANY AND J. KOERTS Lastly, media multitasking behavior might have a reciprocal relationship with problems with cognition and vice versa. On this reciprocity, several longitudinal studies have attempted to examine whether media multitasking behavior and everyday-related problems are reinforcing each other over a longer time period. The results of these studies showed that media multi- tasking did not appear to have a reciprocal relationship with the occurrence attentional problems (Baumgartner, van der Schuur, Lemmens, & Te Poel, 2017) 3 and 6 months later. Nevertheless, these studies showed that the associations between media multitasking and attentional problems were stable over time. That is, the correlation remained significant during the first, second, and third periods of data collection. Together, this might indicate that individuals have a stable level of media multitasking behavior over time and similarly, the occurrence of some everyday-related problems is also stable over time. Limitation and future directions The findings in our set of mini-meta-analyses are limited in several ways. First, while the effects found in different groups of findings were somewhat reliable across different studies, critically, the overall pooled effects were weak, with z ranging from .16 to .22. Thus, most of the variance underlying the media multitasking behavior is still unaccounted for. Additionally, while we refer to the literature, our categorization of effect sizes remained some- what subjective. This subjectivity might introduce bias and or contribute to our level of within-theme heterogeneity. We witnessed a high level of hetero- geneity in the attention regulation theme, and this high heterogeneity could not be explained by our moderators. Arguably, this high level of heteroge- neity might be driven by the effect sizes related to CFQ, which dimension- ality is still being argued for in recent studies (e.g., Bridger et al., 2013). It might be that the null and negative correlations in the CFQ-related effect sizes were driven by the other dimensions of CFQ. Third, the current meta-analysis focuses on studies pertained to everyday cognitive functioning. However, media multitasking studies have gone beyond the cognition-related themes, as some studies have investigated the correlates between media multitasking and depression (Becker, Alzahabi, & Hopwood, 2013), anxiety (Becker et al., 2013; Hatchel et al., 2018), creativity and imagination (Duff et al., 2014), and well-being (Pea et al., 2012; Shih, 2013; Xu, Wang, & David, 2016). With regards to depression and anxiety, heavy media multitasking was correlated with more (severe) depressive and anxiety symptoms (Becker et al., 2013; Hatchel et al., 2018). This finding was somewhat consistent with a recent nation-wide study which also showed that individuals who use multiple social media platforms in daily life had higher odds of having increased levels of depression and anxiety (Primack et al., MEDIA PSYCHOLOGY 19 2017). Furthermore, media multitasking was negatively correlated with ima- gination, but it was positively correlated with creativity (Duff et al., 2014). Lastly, the correlations between media multitasking and well-being were somewhat mixed. In a large-scale study which involved 3461 8-12-year-old girls, Pea et al. (2012) found that media multitasking was positively corre- lated with social success, but it was negatively correlated with normalcy feelings, positive feelings, and social stress. Shih (2013) found that media multitasking was not correlated with well-being as assessed using two ver- sions of self-report questionnaires which focused on well-being. While part of these findings was discouraging, suggesting that media multitasking beha- vior might have potential ramifications on other aspects of everyday func- tioning beyond cognition, the other part, namely the positive correlations with social success and creativity suggests that media multitasking behavior might be beneficial as well. It could be interesting for future studies to further examine the adaptive values of everyday media multitasking behavior, espe- cially given that several longitudinal studies have indicated that media multi- tasking behavior is stable over time (Baumgartner, van der Schuur et al., 2017; van der Schuur et al., 2018). Fourth and lastly, since all findings we synthesized in the meta-analysis were correlational, it is still an open question whether media multitasking behavior leads to, is an effect, or have a reciprocal relationship with the occurrence of cognitive problems in everyday situations. Futures studies might be interested in disentangling this association in a more controlled manner. Conclusion In a series of mini meta-analyses, we categorized the correlates between media multitasking and everyday cognitive functioning, as assessed using self-reports, in four different themes. Heavier media multitasking was asso- ciated with increased of levels of self-reported problems with attention regulation, behavior regulation, impulsiveness/inhibition, and memory. Together, media multitasking appears to be correlated with increasing pro- blems everyday cognitive functioning. However, the overall small effects were small, a high level of heterogeneity was detected in one theme, and a large proportion of variance of media multitasking behavior is still unaccounted for. Additionally, since most studies reported correlations, the causality direction is still unclear. Notes 1. To ensure that all possible relevant results have been included in the meta-analysis, in addition to these keywords, we performed a search using more general keywords, 20 W. WIRADHANY AND J. KOERTS namely media multitask* AND (cognition OR emotion OR trait). This search yielded no additional results. Lists of the references found using our search terms can be found in the supplementary materials of this article. 2. The author was contacted for requesting the relevant zero-order correlations not reported in the article. Unfortunately, due to unforeseen circumstances, the original dataset was no longer available. Nevertheless, we are thankful to Dr. Shui-I Shih for her cooperation. 3. Two of the studies (Baumgartner, van der Schuur, et al., 2017) were longitudinal studies with three waves each. All study waves were included (see Table 1). 4. Note that while this measure has impulsivity on its name, the scale was intended to measure how an individual may act (e.g., suppressing their impulse) in situational contexts (Tsukayama et al., 2013, p. 880). These authors also proposed a distinction between domain-specific impulsivity, which is externally driven and measured by their scale and domain-general impulsivity, which is more internally driven and measured by other impulsivity scales such as the BIS. Acknowledgments This article was written as a part of the Ph. D. project of the first author, which is funded by the Endowment Fund for Education (LPDP), Ministry of Finance, the Republic of Indonesia. We thank Prof. Pedro Cardoso-Leite and Dr. Melina Uncapher for providing us additional data regarding their ADHD findings. Disclosure statement No potential conflict of interest was reported by the authors. Funding This work was supported by the Indonesia Endowment Fund for Education; ORCID Wisnu Wiradhany http://orcid.org/0000-0001-8707-3146 Janneke Koerts http://orcid.org/0000-0002-2317-0171 References Aagaard, J. (2015). Media multitasking, attention, and distraction: A critical discussion. Phenomenology and the Cognitive Sciences, 14(4), 885–896. doi:10.1007/s11097-014-9375-x Alzahabi, R., & Becker, M. W. (2013). The association between media multitasking, task-switching, and dual-task performance. Journal of Experimental Psychology: Human Perception and Performance, 39(5), 1485–1495. doi:10.1037/a0031208 Alzahabi, R., Becker, M. W., & Hambrick, D. Z. (2017). Investigating the relationship between media multitasking and processes involved in task-switching. Journal of Experimental Psychology: Human Perception and Performance, 43(11), 1872–1894. doi:10.1037/ xhp0000412 MEDIA PSYCHOLOGY 21 https://doi.org/10.1007/s11097-014-9375-x https://doi.org/10.1037/a0031208 https://doi.org/10.1037/xhp0000412 https://doi.org/10.1037/xhp0000412 Bardhi, F., Rohm, A. J., & Sultan, F. (2010). Tuning in and tuning out: Media multitasking among young consumers. Journal of Consumer Behaviour, 9, 316–332. doi:10.1002/cb Baumgartner, S. E., Lemmens, J. S., Weeda, W. D., & Huizinga, M. (2017). Measuring media multitasking. Journal of Media Psychology, 29, 188–197. doi:10.1027/1864-1105/a000167 Baumgartner, S. E., van der Schuur, W. A., Lemmens, J. S., & Te Poel, F. (2017). The relationship between media multitasking and attention problems in adolescents: Results of two longitudinal studies. Human Communication Research, 1–27. doi:10.1111/ hcre.12111 Baumgartner, S. E., Weeda, W. D., van der Heijden, L. L., & Huizinga, M. (2014). The relationship between media multitasking and executive function in early adolescents. The Journal of Early Adolescence, 34(8), 1120–1144. doi:10.1177/0272431614523133 Becker, M. W., Alzahabi, R., & Hopwood, C. J. (2013). Media multitasking is associated with symptoms of depression and social anxiety. Cyberpsychology, Behavior and Social Networking, 16(2), 132–135. doi:10.1089/cyber.2012.0291 Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. West Sussex, UK: John Wiley & Sons. Botvinick, M. M., Braver, Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624–652. doi:10.1037// 0033-295X.I08.3.624 Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8(12), 539–546. doi:10.1016/j. tics.2004.10.003 Brasel, S. A., & Gips, J. (2011). Media multitasking behavior: Concurrent television and computer usage. Cyberpsychology, Behavior and Social Networking, 14(9), 527–534. doi:10.1089/cyber.2010.0350 Bridger, R. S., Johnsen, S. Å. K., & Brasher, K. (2013). Psychometric properties of the cognitive failures questionnaire†. Ergonomics, 56(10), 1515–1524. doi:10.1080/00140139.2013.821172 Broadbent, D., & Cooper, P. (1982). The cognitive failures questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21(1), 1–16. Retrieved from http://online library.wiley.com/doi/10.1111/j.2044-8260.1982.tb01421.x/full Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Jounal of Personality and Social Psychology, 84(4), 822–848. doi:10.1037/0022-3514.84.4.822 Burgess, P.W., Alderman, N., Forbes, C., Costello, A., Coates, L.M., Dawson, D. R.,…Channon, S. (2006). The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology. Journal of the International Neuropsychological Society : JINS, 12(2), 194–209. doi:10.1017/S1355617706060310 Cain, M. S., Leonard, J. A., Gabrieli, J. D. E., & Finn, A. S. (2016). Media multitasking in adolescence. Psychonomic Bulletin & Review, 23(6), 1932–1941. doi:10.3758/s13423-016- 1036-3 Cain, M. S., & Mitroff, S. R. (2011). Distractor filtering in media multitaskers. Perception, 40 (10), 1183–1192. doi:10.1068/p7017 Calamaro, C. J., Mason, T. B. A., & Ratcliffe, S. J. (2009). Adolescents living the 24/7 lifestyle: Effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics, 123(6), e1005–e1010. doi:10.1542/peds.2008-3641 Cardoso-Leite, P., Kludt, R., Vignola, G., Ma, W. J., Green, C. S., & Bavelier, D. (2015). Technology consumption and cognitive control: Contrasting action video game experience with media multitasking. Attention, Perception, & Psychophysics, 78(1), 218–241. doi:10.3758/s13414-015-0988-0 22 W. WIRADHANY AND J. KOERTS https://doi.org/10.1002/cb https://doi.org/10.1027/1864-1105/a000167 https://doi.org/10.1111/hcre.12111 https://doi.org/10.1111/hcre.12111 https://doi.org/10.1177/0272431614523133 https://doi.org/10.1089/cyber.2012.0291 https://doi.org/10.1037//0033-295X.I08.3.624 https://doi.org/10.1037//0033-295X.I08.3.624 https://doi.org/10.1016/j.tics.2004.10.003 https://doi.org/10.1016/j.tics.2004.10.003 https://doi.org/10.1089/cyber.2010.0350 https://doi.org/10.1080/00140139.2013.821172 http://onlinelibrary.wiley.com/doi/10.1111/j.2044-8260.1982.tb01421.x/full http://onlinelibrary.wiley.com/doi/10.1111/j.2044-8260.1982.tb01421.x/full https://doi.org/10.1037/0022-3514.84.4.822 https://doi.org/10.1017/S1355617706060310 https://doi.org/10.3758/s13423-016-1036-3 https://doi.org/10.3758/s13423-016-1036-3 https://doi.org/10.1068/p7017 https://doi.org/10.1542/peds.2008-3641 https://doi.org/10.3758/s13414-015-0988-0 Carrier, L. M., Cheever, N. A., Rosen, L. D., Benitez, S., & Chang, J. (2009). Multitasking across generations: Multitasking choices and difficulty ratings in three generations of Americans. Computers in Human Behavior, 25(2), 483–489. doi:10.1016/j.chb.2008.10.012 Carrier, L. M., Rosen, L. D., Cheever, N. A., & Lim, A. F. (2015). Causes, effects, and practicalities of everyday multitasking. Developmental Review, 35, 64–78. doi:10.1016/j. dr.2014.12.005 Carriere, J. S. A., Cheyne, J. A., & Smilek, D. (2008). Everyday attention lapses and memory failures: The affective consequences of mindlessness. Consciousness and Cognition, 17(3), 835–847. doi:10.1016/j.concog.2007.04.008 Carriere, J. S. A., Seli, P., & Smilek, D. (2013). Wandering in both mind and body: Individual differences in mind wandering and inattention predict fidgeting. Canadian Journal of Experimental Psychology/Revue Canadienne De Psychologie Expérimentale, 67(1), 19–31. doi:10.1037/a0031438 Chan, R. C. K., Shum, D., Toulopoulou, T., & Chen, E. Y. H. (2008). Assessment of executive functions: Review of instruments and identification of critical issues. Archives of Clinical Neuropsychology, 23(2), 201–216. doi:10.1016/j.acn.2007.08.010 Christoff, K., Irving, Z. C., Fox, K. C. R., Spreng, R. N., & Andrews-Hanna, J. R. (2016). Mind-wandering as spontaneous thought: A dynamic framework. Nature Reviews Neuroscience, 17(11), 1–44. doi:10.1038/nrn.2016.113 Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron, 69(4), 680–694. doi:10.1016/j.neuron.2011.01.020 Dalley, J. W., & Robbins, T. W. (2017). Fractionating impulsivity: Neuropsychiatric implications. Nature Reviews Neuroscience, 18(3), 158–171. doi:10.1038/nrn.2017.8 Diamond, A. (2013). Executive functions. The Annual Review of Psychology, 64, 135–168. doi:10.1146/annurev-psych-113011-143750 Duff, B. R.-L., Yoon, G., Wang, Z., (Glenn), & Anghelcev, G. (2014). Doing it all: An exploratory study of predictors of media multitasking. Journal of Interactive Advertising, 14(1), 11–23. 10.1080/15252019.2014.884480 Edwards, K. S., & Shin, M. (2017). Media multitasking and implicit learning. Attention, Perception, & Psychophysics, 79(5), 1535–1549. doi:10.3758/s13414-017-1319-4 Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical Research ed.), 315(7109), 629–634. doi:10.1136/bmj.316.7129.469 Engle, R. W. (2002). Working memory capacity as executive attention. Psychological Science, 11(1), 19–23. doi:10.1111/1467-8721.00160 Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory capacity, and a two-factor theory of cognitive control. The Psychology of Learning and Motivation, 44, 145–199. doi:10.1016/S0079-7421(03)44005-X Farias, S. T., Harrell, E., Neumann, C., & Houtz, A. (2003). The relationship between neuropsychological performance and daily functioning in individuals with Alzheimer’s disease: Ecological validity of neuropsychological tests. Archives of Clinical Neuropsychology, 18(6), 655–672. doi:10.1016/S0887-6177(02)00159-2 Fox, A. B., Rosen, J., & Crawford, M. (2009). Distractions, distractions: Does instant messa- ging affect college students’ performance on a concurrent reading comprehension task? Cyberpsychology & Behavior : the Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 12(1), 51–53. doi:10.1089/cpb.2008.0107 Gioia, G. A., Isquith, P. K., Guy, S. C., & Kenworthy, L. (2000). TEST REVIEW behavior rating inventory of executive function behavior rating inventory of executive function. Child Neuropsychology, 6(3), 235–238. doi:10.1076/chin.6.3.235.3152 MEDIA PSYCHOLOGY 23 https://doi.org/10.1016/j.chb.2008.10.012 https://doi.org/10.1016/j.dr.2014.12.005 https://doi.org/10.1016/j.dr.2014.12.005 https://doi.org/10.1016/j.concog.2007.04.008 https://doi.org/10.1037/a0031438 https://doi.org/10.1016/j.acn.2007.08.010 https://doi.org/10.1038/nrn.2016.113 https://doi.org/10.1016/j.neuron.2011.01.020 https://doi.org/10.1038/nrn.2017.8 https://doi.org/10.1146/annurev-psych-113011-143750 http://10.1080/15252019.2014.884480 https://doi.org/10.3758/s13414-017-1319-4 https://doi.org/10.1136/bmj.316.7129.469 https://doi.org/10.1111/1467-8721.00160 https://doi.org/10.1016/S0079-7421(03)44005-X https://doi.org/10.1016/S0887-6177(02)00159-2 https://doi.org/10.1089/cpb.2008.0107 https://doi.org/10.1076/chin.6.3.235.3152 Gioia, G. A., Isquith, P. K., Retzlaff, P. D., & Espy, K. A. (2002). Confirmatory factor analysis of the behavior rating inventory of executive function (BRIEF) in a clinical sample. Child Neuropsychology, 8(4), 249–257. doi:10.1076/chin.8.4.249.13513 González, V. M., & Mark, G. (2004). “Constant, constant, multi-tasking craziness”: Managing multiple working spheres. CHI ’04 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 6(1), 113–120. doi:10.1145/985692.985707 Gorman, T. E., & Green, C. S. (2016). Short-term mindfulness intervention reduces the negative attentional effects associated with heavy media multitasking. Scientific Reports, 6 (April), 24542. doi:10.1038/srep24542 Hadlington, L., & Murphy, K. (2018). Is media multitasking good for cybersecurity? Exploring the relationship between media multitasking and everyday cognitive failures on self-reported risky cybersecurity behaviors. Cyberpsychology, Behavior and Social Networking, 21(3), 168–172. doi:10.1089/cyber.2017.0524 Hatchel, T., Negriff, S., & Subrahmanyam, K. (2018). The relation between media multi- tasking, intensity of use, and well-being in a sample of ethnically diverse emerging adults. Computers in Human Behavior, 81, 115–123. doi:10.1016/j.chb.2017.12.012 Hedges, L. V., & Pigott, T. D. (2004). The power of statistical tests for moderators in meta-analysis. Psychological Methods, 9(4), 426–445. doi:10.1037/1082-989X.9.4.426 Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39–65. doi:10.1002/jrsm.5 Heppner, P. P., & Petersen, C. H. (1982). The development and implications of a personal problem-solving inventory. Journal of Counseling Psychology, 29(1), 66–75. doi:10.1037/ 0022-0167.29.1.66 Hoyle, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P., & Donohew, R. L. (2002). Reliability and validity of a brief measure of sensation seeking. Personality and Individual Differences, 32(3), 401–414. doi:10.1016/S0191-8869(01)00032-0 Huizinga, M., & Smidts, D. P. (2010). Age-related changes in executive function: A normative study with the Dutch version of the Behavior Rating Inventory of Executive Function (BRIEF). Child Neuropsychology, 17(1), 51–66. doi:10.1080/09297049.2010.509715 Hwang, Y., Kim, H., & Jeong, S. H. (2014). Why do media users multitask?: Motives for general, medium-specific, and content-specific types of multitasking. Computers in Human Behavior, 36, 542–548. doi:10.1016/j.chb.2014.04.040 Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2 (8), e124. doi:10.1371/journal.pmed.0020124 Ioannidis, J. P. A., Munafò, M. R., Fusar-Poli, P., Nosek, B. A., & David, S. P. (2014). Publication and other reporting biases in cognitive sciences: Detection, prevalence, and prevention. Trends in Cognitive Sciences, 18(5), 235–241. doi:10.1016/j.tics.2014.02.010 Jeong, S.-H., & Hwang, Y. (2016). Media multitasking effects on cognitive vs. Attitudinal outcomes: A meta-analysis. Human Communication Research, 42(4), 599–618. doi:10.1111/ hcre.12089 Judd, T. (2013). Making sense of multitasking: Key behaviours. Computers and Education, 63, 358–367. doi:10.1016/j.compedu.2012.12.017 Kazakova, S., Cauberghe, V., Pandelaere, M., & De Pelsmacker, P. (2015). Can’t see the forest for the trees? The effect of media multitasking on cognitive processing style. Media Psychology, 18(4), 425–450. doi:10.1080/15213269.2015.1006789 Kessler, R. C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E., … Walters, E. E. (2005). The world health organization adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. Psychological Medicine, 35(2), 245–256. doi:10.1017/S0033291704002892 24 W. WIRADHANY AND J. KOERTS https://doi.org/10.1076/chin.8.4.249.13513 https://doi.org/10.1145/985692.985707 https://doi.org/10.1038/srep24542 https://doi.org/10.1089/cyber.2017.0524 https://doi.org/10.1016/j.chb.2017.12.012 https://doi.org/10.1037/1082-989X.9.4.426 https://doi.org/10.1002/jrsm.5 https://doi.org/10.1037/0022-0167.29.1.66 https://doi.org/10.1037/0022-0167.29.1.66 https://doi.org/10.1016/S0191-8869(01)00032-0 https://doi.org/10.1080/09297049.2010.509715 https://doi.org/10.1016/j.chb.2014.04.040 https://doi.org/10.1371/journal.pmed.0020124 https://doi.org/10.1016/j.tics.2014.02.010 https://doi.org/10.1111/hcre.12089 https://doi.org/10.1111/hcre.12089 https://doi.org/10.1016/j.compedu.2012.12.017 https://doi.org/10.1080/15213269.2015.1006789 https://doi.org/10.1017/S0033291704002892 Kononova, A., Joo, E., & Yuan, S. (2016). If I choose when to switch: Heavy multitaskers remember online content better than light multitaskers when they have the freedom to multitask. Computers in Human Behavior, 65, 567–575. doi:10.1016/j.chb.2016.09.011 Kononova, A., Zasorina, T., Diveeva, N., Kokoeva, A., & Chelokyan, A. (2014). Multitasking goes global: Multitasking with traditional and new electronic media and attention to media messages among college students in Kuwait, Russia, and the USA. International Communication Gazette, 76(8), 617–640. doi:10.1177/1748048514548533 Larson, G. E., Alderton, D. L., Neideffer, M., & Underhill, E. (2011). Further evidence on dimensionality and correlates of the cognitive failures questionnaire. British Journal of Psychology, 88(1), 29–38. doi:10.1111/j.2044-8295.1997.tb02618.x Lenhart, A. (2015). Teens, social media & technology overview 2015. In Pew Research Center’s Internet & American Life Project. Retrieved from http://www.pewinternet.org/2015/04/09/ teens-social-media-technology-2015/ Lin, L. (2009). Breadth-biased versus focused cognitive control in media multitasking behaviors. Proceedings of the National Academy of Sciences of the United States of America, 106(37), 15521–15522. doi:10.1073/pnas.0908642106 Lin, L., Robertson, T., & Lee, J. (2009). Reading performances between novices and experts in different media multitasking environments. Computers in the Schools, 26(3), 169–186. doi:10.1080/07380560903095162 Loh, K. K., & Kanai, R. (2014). Higher media multi-tasking activity is associated with smaller gray-matter density in the anterior cingulate cortex. PloS One, 9(9), e106698. doi:10.1371/ journal.pone.0106698 Loh, K. K., Tan, B. Z. H., & Lim, S. W. H. (2016). Media multitasking predicts video-recorded lecture learning performance through mind wandering tendencies. Computers in Human Behavior, 63, 943–947. doi:10.1016/j.chb.2016.06.030 Lui, K. F. H., & Wong, A. C.-N. (2012). Does media multitasking always hurt? A positive correlation between multitasking and multisensory integration. Psychonomic Bulletin & Review, 19(4), 647–653. doi:10.3758/s13423-012-0245-7 Magen, H. (2017). The relations between executive functions, media multitasking and polychronicity. Computers in Human Behavior, 67, 1–9. doi:10.1016/j.chb.2016.10.011 Marius, P., & Anggoro, S. (2014). Profil Pengguna Internet Indonesia 2014. Jakarta, Indonesia: Asosiasi Penyelenggara Jasa Internet Indonesia. Mark, G., Gudith, D., & Klocke, U. (2008). The cost of interrupted work: More speed and stress. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 107–110. doi:10.1145/1357054.1357072 Minear, M., Brasher, F., McCurdy, M., Lewis, J., & Younggren, A. (2013). Working memory, fluid intelligence, and impulsiveness in heavy media multitaskers. Psychonomic Bulletin & Review, 20, 1274–1281. doi:10.3758/s13423-013-0456-6 Moisala, M., Salmela, V., Hietajärvi, L., Salo, E., Carlson, S., Salonen, O., … Alho, K. (2016). Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. NeuroImage, 134, 113–121. doi:10.1016/j. neuroimage.2016.04.011 Murphy, K., McLauchlan, S., & Lee, M. (2017). Is there a link between media-multitasking and the executive functions of filtering and response inhibition? Computers in Human Behavior, 75, 667–677. doi:10.1016/j.chb.2017.06.001 Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences of the United States of America, 106(37), 15583–15587. doi:10.1073/pnas.0903620106 MEDIA PSYCHOLOGY 25 https://doi.org/10.1016/j.chb.2016.09.011 https://doi.org/10.1177/1748048514548533 https://doi.org/10.1111/j.2044-8295.1997.tb02618.x http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/ http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/ https://doi.org/10.1073/pnas.0908642106 https://doi.org/10.1080/07380560903095162 https://doi.org/10.1371/journal.pone.0106698 https://doi.org/10.1371/journal.pone.0106698 https://doi.org/10.1016/j.chb.2016.06.030 https://doi.org/10.3758/s13423-012-0245-7 https://doi.org/10.1016/j.chb.2016.10.011 https://doi.org/10.1145/1357054.1357072 https://doi.org/10.3758/s13423-013-0456-6 https://doi.org/10.1016/j.neuroimage.2016.04.011 https://doi.org/10.1016/j.neuroimage.2016.04.011 https://doi.org/10.1016/j.chb.2017.06.001 https://doi.org/10.1073/pnas.0903620106 Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the barratt impulsiveness scale. Journal of Clinical Psychology, 51, 768–774. doi:10.1002/(ISSN)1097- 4679 Pea, R., Nass, C., Meheula, L., Rance, M., Kumar, A., Bamford, H., … Zhou, M. (2012). Media use, face-to-face communication, media multitasking, and social well-being among 8- to 12-year-old girls. Developmental Psychology, 48(2), 327–336. doi:10.1037/a0027030 Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35(1), 73–89. doi:10.1146/annurev-neuro-062111- 150525 Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42. doi:10.1146/annurev.ne.13.030190.000325 Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among U.S. young adults. Computers in Human Behavior, 69, 1–9. doi:10.1016/j.chb.2016.11.013 R Core team. (2015). R Core Team. R: A Language and Environment for Statistical Computing., 55, 275–286. Retrieved from http://www.r-project.org/ Ralph, B. C. W., & Smilek, D. (2016). Individual differences in media multitasking and performance on the n-back. Attention, Perception, & Psychophysics, 79(2), 582–592. doi:10.3758/s13414-016-1260-y Ralph, B. C. W., Thomson, D. R., Cheyne, J. A., & Smilek, D. (2013). Media multitasking and failures of attention in everyday life. Psychological Research, 78(5), 661–669. doi:10.1007/ s00426-013-0523-7 Ralph, B. C. W., Thomson, D. R., Seli, P., Carriere, J. S. A., & Smilek, D. (2015). Media multitasking and behavioral measures of sustained attention. Attention, Perception, & Psychophysics, 77(2), 390–401. doi:10.3758/s13414-014-0771-7 Rideout, V. J., Foehr, U. G., & Roberts, D. F. (2010). Generation M2: Media in the Lives of 8 to 18 Year-Olds. Retrieved from https://files.eric.ed.gov/fulltext/ED527859.pdf Rigby, J. M., Brumby, D. P., Gould, S. J. J., & Cox, A. L. (2017). Media multitasking at home: A video observation study of concurrent TV and mobile device usage. Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video, 3–10. doi:10.1145/3077548.3077560 Roberts, D. F., & Foehr, U. G. (2008). Trends in media use. Future of Children, 18(1), 11–37. doi:10.1353/foc.0.0000 Sanbonmatsu, D. M., Strayer, D. L., Medeiros-Ward, N., & Watson, J. M. (2013). Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking. PloS One, 8(1), e54402. doi:10.1371/journal.pone.0054402 Scammacca, N., Roberts, G., & Stuebing, K. K. (2013). Meta-analysis with complex research designs: Dealing with dependence from multiple measures and multiple group comparisons. Review of Educational Research, 84(3), 0034654313500826. doi:10.3102/ 0034654313500826 Schutten, D., Stokes, K. A., & Arnell, K. M. (2017). I want to media multitask and I want to do it now: Individual differences in media multitasking predict delay of gratification and system-1 thinking. Cognitive Research: Principles and Implications, 2(1), 8. doi:10.1186/ s41235-016-0048-x Shih, S. I. (2013). A null relationship between media multitasking and well-being. PloS One, 8, 5. doi:10.1371/journal.pone.0064508 Smallwood, J., & Schooler, J. W. (2006). The restless mind. Psychological Bulletin, 132(6), 946–958. doi:10.1037/0033-2909.132.6.946 26 W. WIRADHANY AND J. KOERTS https://doi.org/10.1002/(ISSN)1097-4679 https://doi.org/10.1002/(ISSN)1097-4679 https://doi.org/10.1037/a0027030 https://doi.org/10.1146/annurev-neuro-062111-150525 https://doi.org/10.1146/annurev-neuro-062111-150525 https://doi.org/10.1146/annurev.ne.13.030190.000325 https://doi.org/10.1016/j.chb.2016.11.013 http://www.r-project.org/ https://doi.org/10.3758/s13414-016-1260-y https://doi.org/10.1007/s00426-013-0523-7 https://doi.org/10.1007/s00426-013-0523-7 https://doi.org/10.3758/s13414-014-0771-7 https://doi.org/10.1145/3077548.3077560 https://doi.org/10.1353/foc.0.0000 https://doi.org/10.1371/journal.pone.0054402 https://doi.org/10.3102/0034654313500826 https://doi.org/10.3102/0034654313500826 https://doi.org/10.1186/s41235-016-0048-x https://doi.org/10.1186/s41235-016-0048-x https://doi.org/10.1371/journal.pone.0064508 https://doi.org/10.1037/0033-2909.132.6.946 Stanovich, K. E. (2009). Distinguishing the reflective, algorithmic, and autonomous minds: Is it time for a tri-process theory? In J. S. B. T. Evans & K. Frankish (Eds.), In two minds: Dual processes and beyond (pp. 55–88). New York, NY: Oxford University Press. doi:10.1093/acprof:oso/9780199230167.003.0003 Sterne, J. A. C., Gavaghan, D., & Egger, M. (2000). Publication and related bias in meta-analysis: Power of statistical tests and prevalence in the literature. Journal of Clinical Epidemiology, 53(11), 1119–1129. doi:10.1016/S0895-4356(00)00242-0 Toplak, M. E., West, R. F., & Stanovich, K. E. (2013). Practitioner review: Do performance-based measures and ratings of executive function assess the same construct? Journal of Child Psychology and Psychiatry and Allied Disciplines, 54(2), 131–143. doi:10.1111/jcpp.12001 Tsukayama, E., Duckworth, A. L., & Kim, B. (2013). Domain-specific impulsivity in school-age children. Developmental Science, 16(6), 879–893. doi:10.1111/desc.12067 Uncapher, M. R., Thieu, M. K., & Wagner, A. D. (2016). Media multitasking and memory: Differences in working memory and long-term memory. Psychonomic Bulletin & Review, 23(2), 483–490. doi:10.3758/s13423-015-0907-3 van der Schuur, W. A., Baumgartner, S. E., Sumter, S. R., & Valkenburg, P. M. (2015). The consequences of media multitasking for youth: A review. Computers in Human Behavior, 53, 204–215. doi:10.1016/j.chb.2015.06.035 van der Schuur, W. A., Baumgartner, S. E., Sumter, S. R., & Valkenburg, P. M. (2018). Media multitasking and sleep problems: A longitudinal study among adolescents. Computers in Human Behavior, 81, 316–324. doi:10.1016/j.chb.2017.12.024 Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor Package. Journal of Statistical Software, 36(3), 1–48. doi:10.18637/jss.v036.i03 Wallace, J. C., Kass, S. J., & Stanny, C. J. (2002). The cognitive failures questionnaire revisited: Dimensions and correlates. Journal of General Psychology, 129(3), 238–256. doi:10.1080/ 00221300209602098 Wang, Z., & Tchernev, J. M. (2009). The “Myth” of media multitasking: reciprocal dynamics of media multitasking, personal needs, and gratifications. In Journal of Communication, 62, 493–513. doi:10.1111/j.1460-2466.2012.01641.x Whiteside, S. P., & Lynam, D. R. (2001). The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30(4), 669–689. doi:10.1016/S0191-8869(00)00064-7 Wiradhany, W., & Nieuwenstein, M. R. (2017). Cognitive control in media multitaskers: Two replication studies and a meta-Analysis. Attention, Perception, & Psychophysics, 79(8), 2620–2641. doi:10.3758/s13414-017-1408-4 Wiradhany, W., van Vugt, M. K., & Nieuwenstein, M. R. (2019). Media multitasking, mind-wandering, and distractibility: A large-scale study. Attention, Perception & Psychophysics. doi:10.3758/s13414-019-01842-0 World Health Organization. (2001). International classification of functioning, disability and health: Short version. Retrieved from http://apps.who.int/iris/bitstream/10665/42417/1/ 9241545445_eng.pdf Wu, J. Y. (2017). The indirect relationship of media multitasking self-efficacy on learning performance within the personal learning environment: Implications from the mechanism of perceived attention problems and self-regulation strategies. Computers and Education, 106, 56–72. doi:10.1016/j.compedu.2016.10.010 Xu, S., Wang, Z., & David, P. (2016). Media multitasking and well-being of university students. Computers in Human Behavior, 55, 242–250. doi:10.1016/j.chb.2015.08.040 Yang, X., & Zhu, L. (2016). Predictors of media multitasking in Chinese adolescents. International Journal of Psychology, 51(6), 430–438. doi:10.1002/ijop.12187 MEDIA PSYCHOLOGY 27 https://doi.org/10.1093/acprof:oso/9780199230167.003.0003 https://doi.org/10.1016/S0895-4356(00)00242-0 https://doi.org/10.1111/jcpp.12001 https://doi.org/10.1111/desc.12067 https://doi.org/10.3758/s13423-015-0907-3 https://doi.org/10.1016/j.chb.2015.06.035 https://doi.org/10.1016/j.chb.2017.12.024 https://doi.org/10.18637/jss.v036.i03 https://doi.org/10.1080/00221300209602098 https://doi.org/10.1080/00221300209602098 https://doi.org/10.1111/j.1460-2466.2012.01641.x https://doi.org/10.1016/S0191-8869(00)00064-7 https://doi.org/10.3758/s13414-017-1408-4 https://doi.org/10.3758/s13414-019-01842-0 http://apps.who.int/iris/bitstream/10665/42417/1/9241545445_eng.pdf http://apps.who.int/iris/bitstream/10665/42417/1/9241545445_eng.pdf https://doi.org/10.1016/j.compedu.2016.10.010 https://doi.org/10.1016/j.chb.2015.08.040 https://doi.org/10.1002/ijop.12187 Yap, J. J. Y., & Lim, S. S. W. H. (2013). Media multitasking predicts unitary versus splitting visual focal attention. Journal of Cognitive Psychology, 25(7), 889–902. doi:10.1080/ 20445911.2013.835315 Zuckerman, M. (1996). Item revisions in the Sensation Seeking Scale Form V (SSS-V). Personality and Individual Differences, 20(4), 515. doi:10.1016/0191-8869(95)00195-6 Zuckerman, M. (2007). Sensation seeking and risky behavior. Washington, DC: American Psychological Association. doi:10.1037/11555-000 28 W. WIRADHANY AND J. KOERTS https://doi.org/10.1080/20445911.2013.835315 https://doi.org/10.1080/20445911.2013.835315 https://doi.org/10.1016/0191-8869(95)00195-6 https://doi.org/10.1037/11555-000 Abstract Correlates of media multitasking The current study Methods Study selection Effect size selection and calculation Analysis Categorization of findings Random-effect model Heterogeneity and risk of bias Results Attention regulation Random-effect model Heterogeneity & risk of bias analysis Impulsiveness– inhibition Random-effect model Memory Random-effect model Behavior regulation Random-effect model General discussion Notes on causality Limitation and future directions Conclusion Notes Acknowledgments Disclosure statement Funding References
The Public-Private Mix in the Delivery of Health-Care Services: Its Relevance for Lower-Income Canadians Gregory P. Marchildon1 & Sara Allin1,2 Published online: 29 July 2016 # Springer International Publishing 2016 Abstract This paper reviews and analyzes the implica- tion of the public-private mix of financing and delivery of health care in Canada for lower-income Canadians. Based on the type of government stewardship and the degree of state intervention, the Canadian health system can be separated into three distinct layers: universal hospital and physician services financed and regulated by federal and provincial governments (BMedicare^); mixed services, including prescription drugs and long- term care, subject to some provincial stewardship and subsidy; and privately funded and delivered services such as dental care. Within Medicare financial barriers to access have been removed; however, there is a grow- ing trend toward private sector involvement in the de- livery of services, and inequalities by income in the use of physician services are high in Canada relative to other high income countries. Moreover, the exclusion of prescription drugs and long-term care from universal health coverage in Canada, as well as the nearly exclu- sively private dental market, has created significant ac- cess issues for lower-income Canadians. Keywords Canadian health system . Lower-income . Public-private mix Introduction: an Overall Health System Overview Canada is a highly decentralized federation with a similarly decentralized system of health administration and delivery. Although the federal government plays an important role in terms of national standard-setting in some areas of health care and regulation of pharmaceuticals and health products, and for the delivery of some health services to designated populations, it is the provincial governments which are primarily responsi- ble for how most health-care services are delivered to Canadians, including low-income and marginalized groups. By low-income, we generally refer to Canadians in lowest income quartile of the population. As illustrated in Table 1, there are three discernable layers making up the Canadian health system when viewed through the perspective of degree of state involvement. Each layer has its own configuration of funding, administration, and delivery arrangements. As a consequence, the public-private mix in each of the three layers has quite different implications for the delivery of health services to lower-income Canadians. Table 1 purposely separates the factors of funding, admin- istration, and regulation from service delivery to clarify the following discussion (see Deber 2004). There are public and private components in each. Before focusing on the public and private components in health service delivery, it is worth set- ting out some general propositions on the public-private di- vide in funding, administration, and regulation. Canada has a 70:30 split between public and private financ- ing of healthcare. This 70:30 ratio is lower than the public- private ratio of a majority of higher-income countries (CIHI 2015b). Canada, like all high-income welfare states, devotes significant resources to health care. Table 2 compares all wel- fare states that spent, through their central, regional, and local governments, a minimum of US$2500 a year in 2013. These are all countries in which universal health coverage is the * Gregory P. Marchildon [email protected] 1 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada 2 Canadian Institute for Health Information, Ottawa, ON, Canada Glob Soc Welf (2016) 3:161–170 DOI 10.1007/s40609-016-0070-4 http://crossmark.crossref.org/dialog/?doi=10.1007/s40609-016-0070-4&domain=pdf norm (or at least moving toward universal coverage as is the case in the US) and where health care has become a more expensive policy responsibility than those relating to educa- tion or social assistance (welfare). It is generally assumed that public financing of universal health coverage (UHC) is redis- tributive—that UHC shifts resources from the healthy and wealthy to the poor and sick. Sherry Glied (2008) performed the calculation on CanadianMedicare and found that every $1 of tax funding would move between $0.23 and $0.26 toward the lowest income quintile of the population and roughly $0.50 into the two lowest income quintiles. No different than other high-income welfare states, govern- ments in Canada have created an intricate web of administra- tion, law, and regulation to govern and manage universal health coverage. There are two key aspects in the governance of UHC in Canada. The first is the Canada Health Act, the federal law that sets out five national standards with which provincial governments are expected to comply in order to receive their per capita shares of a cash transfer from the fed- eral government (Marchildon 2013). The second key governance aspect is a set of provincial laws, regulations, and accompanying arrangements between these governments and the medical profession that determine how single-payer UHC is actually administered. The laws prohibit or discourage the sale of private health insurance for Medicare services (Flood and Archibald 2001), while the ar- rangements prevent or discourage doctors from working both sides of the public-private street (Flood and Haugen 2010). The end result is that doctors are expected to opt out of the UHC system entirely if they provide private services to pa- tients who choose to pay privately. Consistent with the three layers illustrated in Table 1, the first section of this paper will summarize the delivery of UHC services to low-income Canadians by a mix of public and private providers. The second section will ex- amine one key dimension of social care—institutional (nursing home) long-term care, also a mixed sector in terms of public and private delivery but with the balance tilted more toward public finance but private delivery. This is followed by an analysis of the implications of the public- private mix in prescription drugs where private financing edges out public financing but where delivery involves a range of private actors from professionals (physicians and pharmacists) to pharmaceutical manufacturers and re- tailers. The final section deals with dental care, the most private area of Canadian health care in terms of both funding and delivery. Table 1 Three layers of the Canadian health system based on degree of state intervention Degree of state involvement Funding Administration and regulation Delivery Major—universal health coverage (Medicare) Public taxation through the general revenue funds of federal and provincial governments Single-payer provincial systems. Private self-regulating professions under provincial legislative frameworks Private physician services, private for-profit (very limited), not-for- profit, and arm’s-length organizations delivering hospital services Moderate (social care and prescription drugs) Mix of public taxation, private (mainly employment-based) insurance and out-of-pocket payments Public services that are generally welfare-based and targeted, and private services regulated to varying degrees by provincial governments Private professional, private for- profit, not-for profit, and public arm’s-length facilities and organizations Minimal (dental and vision care, alternative and complementary medicines) Private (mainly employment- based) insurance and out- of-pocket payments Private ownership and control: private professionals with self-regulation. Limited state regulation Private providers and private-for- profit facilities and organizations Source: adapted from Marchildon (2004, p. 63) Table 2 High-income OECDmember states in which total government health spending exceeded US$2500 per year in 2013 OECD country (rank based on spending) Government spending per capita ($US PPP for 2013) 1. Norway 4981 2. Netherlands 4495 3. United States 4198 4. Switzerland 4178 5. Sweden 4126 6. Denmark 3841 7. Germany 3677 8. Austria 3469 9. Belgium 3312 10. France 3247 11. Japan 3090 12. Canada 3074 13. Iceland 2968 14. United Kingdom 2802 15. New Zealand 2656 16. Australia 2614 17. Finland 2583 Source: OECD (2015) 162 Glob Soc Welf (2016) 3:161–170 The Delivery of Canadian Medicare Services The area of greatest state intervention involves those health services that are universally covered and free at the point of access for all Canadians. Known colloquially as Medicare (and not to be confused with similarly named programs in the USA and Australia), UHC was introduced in two major phases in the postwar decades. Originally implemented in the province of Saskatchewan in 1947, a single-payer system of universal hospital coverage was adopted in other provinces between 1958 and 1961 in response to an offer of federal cost-sharing and acceptance of national standards (Marchildon 2012; Taylor 1987). A similar evolution occurred for universal coverage of medically necessary physician services. The government of Saskatchewan piloted the first single-payer program for cov- erage of medically necessary physician costs and then adopted in all other provinces between 1968 and 1971, again in re- sponse to an offer of federal cost-sharing and an acceptance of key national standards (Naylor 1986). These standards in- cluded the portability of provincial coverage and a strong ver- sion of universality that required coverage to be provided on uniform terms and conditions (Marchildon 2014). Although many policy makers assumed at the time that UHC would eventually be extended to health services beyond hospital and physicians, this did not occur. As a consequence, Canadian Medicare is generally defined as deep but nar- row—a reference to the fact that there while there is no cost to the user at the point of service, this deep coverage accom- panied by national standards (including portability of cover- age among provinces) has remained restricted to medically necessary hospital (including in-hospital dental surgery), di- agnostic and physician services. As pointed out above, the national standards of Medicare were reinforced at the provincial government level by the passage of more detailed laws and regulations. There was some policy path dependency in that provincial governments implemented universal medical care coverage in the late 1960s and early 1970s in much the same way they had imple- mented universal hospital coverage a decade earlier. At the delivery level, this meant that all Canadians, irre- spective of income, were to receive the same coverage, the same quality of services on the same criteria of medical necessity rather than ability to pay. This was intended to be one-tier UHC in which all—including the very poor— would receive the same services based on need. Indeed, the main objective of Medicare as a national policy and provin- cial program was to ensure that all Canadians would have uniform access to medically necessary services and that no one be discriminated against on the basis of income or other factors (Romanow 2002). This one-tier system is protected by the five funding criteria of the Canada Health Act de- scribed in Table 3. However, two major OECD studies of high-income coun- tries, including Canada, which had similar objectives through UHC, exhibited evidence of income-related differences in the utilization of health services (Devaux 2016; van Doorslaer and Masseria 2004). Both studies found significant differences by income in the likelihood of visiting a primary care physician in a year after adjusting for need, and an even greater difference by income in the likelihood of visiting a specialist physician. However, low-income groups used hospital services significant- ly more than higher-income groups after adjusting for need (van Doorslaer and Masseria 2004). While the majority of OECD countries demonstrated significant income-related inequalities in physician services, inequity in the probability of visiting a GP and a specialist was among the highest in Canada (Devaux 2016). In other words, lower-incomeCanadians are significantly less likely to report to visit a GP or a specialist in the past year than higher-income Canadians in the same general level of health, and this difference by income is higher in Canada than in other comparable countries. In this literature, the term Bpro- poor^ is used to describe the greater use (or probability of use) of health care in the lower end of the income distribution, after controlling for need, whereas a finding of Bpro-rich^ inequality signals the reverse. Income is usually measured on a continuous scale, so the findings do not point to specific income levels, but rather to the extent that health care use is more concentrated in the lower end (pro-poor) or the upper end (pro-rich) of the in- come distribution of the population. Table 3 Five Criteria of the Canada Health Act (1984) Criteria Each provincial single-payer Medicare plan must: Public administration Be administered and operated on a nonprofit basis by a public authority Comprehensiveness Cover all Medicare services provided by hospitals, physicians, or dentists (restricted to inpatient surgical dental services) and, where a provincial law permits, similar or additional services rendered by other health care providers Universality Ensure entitlement to all Medicare health services on uniform terms and conditions Portability Not impose a minimum period of residence (waiting period) in excess of 3 months for new residents; pay for its own residents visiting another province (or country in the case of nonurgent services) with reimbursement paid at the home rate of province; and cover the waiting period for those residents moving to another province until the new province assumes responsibility (within 3 months) for UHC Accessibility Not impede or preclude, either directly or indirectly, whether by charges made to insured persons or otherwise, reasonable access to Medicare services Source: Adapted from Marchildon (2013, p. 28) Glob Soc Welf (2016) 3:161–170 163 Studies within Canada help to shed some light on the rea- son for the income-related inequalities in physician services that are free at the point of use for all Canadians. Allin’s (2008) study of equity of access in all Canadian provinces found evidence of barriers for lower-income groups in accessing an initial visit to primary care physicians after adjusting for need, but with visits becoming pro-poor after the initial visit. Her hypothesis is that while the initial visit is patient-driven, subsequent visits are more physician-driven and this produces a result in which access is more equally distributed overall. The pro-rich bias in the probability of the initial contact with a GP is in part related to the heavy reliance on private finance for prescription drugs, which are complementary to physician services (Allin and Hurley 2009). In other words, people with lower income are both less likely to hold private insurance for prescription drugs and less able to afford to pay out-of-pocket costs of medications; therefore, they may be deterred from visiting a physician because of the expected costs of drugs (Allin and Hurley 2009). The pro-rich bias could also relate to the fact that many Canadians do not have a regular family doctor, though few studies have tested this explicitly. Survey data from 2014 indi- cate less than 10 % of the population in Ontario report not to have a family doctor, compared to 20 % of the populations in Alberta and Saskatchewan, and 25 % in Quebec (CIHI 2015a). As for specialist visits, an earlier study examined how spe- cialist care favored higher-income—correlated with better ed- ucated—Canadians (Dunlop et al. 2000). Both McGrail’s (2008) British Columbia study and two pan-Canadian studies confirmed a pro-rich bias in the utilization of specialist ser- vices (Allin 2008; Asada and Kephart 2007). However, since Canadians can only obtain specialist services via a referral from a primary care physician, this barrier may be associated with the pro-rich bias in getting a first visit with the physician who can then refer to a specialist for the first time. In fact, Allin (2008) found that the pro-rich specialist inequity was rendered nonsignificant after the first specialist visit, except in two provinces—Prince Edward Island and Alberta. Even after adjusting for need, lower-income groups utilize hospital services more than other income groups although Allin’s pan-Canadian results by province show that this ineq- uity is difficult to establish statistically based on admissions, the number of nights spent in hospital, or the probability of spending one night in hospital (Allin 2008). It is likely that more extensive use of hospitals does not translate to better care, and the higher concentration of hospital use in the lower end of the income distribution may signal a lack of effective primary care (Allin 2008). It is clear that more research is required before it is even possible to speculate on the reasons for any pro-poor inequity in the use of hospital care and in particular to distinguish from potentially avoidable hospitali- zations from needed hospital services. While the literature on inequity in utilization of physician services is quite large in Canada and, internationally, there has been growing interest in examining inequalities by income in other publicly funded services, such as preventive care, the recent OECD study on inequalities by income included mea- sures of cervical and breast cancer screening, and found sig- nificant inequity by income in all OECD countries, with the magnitude of inequity in Canada falling in the middle of the pack (Devaux 2016). In Canada, a literature review on access to cancer care found that income had the most consistent effect on cancer screening rates, while age and geographical inequal- ities were evident in end-of-life care (Maddison et al. 2011). These patterns suggest that higher-income individuals are more likely to take advantage of the provinces’ universal can- cer screening programs. Trends in Terms of the Public or Private Delivery of Medicare There has been a long-term shift from private nonprofit and local government ownership to more provincial government ownership and management of hospitals. This has been done through the introduction of regional health authorities (RHAs) in most provinces. These arm’s-length administrative bodies were created under provincial statute in the early to mid- 1990s. RHAs were mandated to administer and organize the delivery of a broad continuum of health services within spec- ified geographic regions. Recent years have seen a trend to- ward centralization as provincial governments reduced the number of RHAs, increasing the size of the populations served by each RHA. In three provinces, single health delivery orga- nizations covering the entire provincial population have re- placed the geographic-based RHAs. However, whether decentralized or centralized, RHAs own and manage most of the hospitals located within their respec- tive borders. A few religious-based hospitals continue to have independent ownership and management, but these organiza- tions operate under contract with RHAs and are coordinated as part of a larger health system. Before the 1990s, almost all hospitals in Canada were owned and managed by private (mainly nonprofit) organizations. Ontario is the only province in which this structure continues. This ownership and man- agement structure was not altered with the introduction of Local Health Integration Networks (LHINs), which were then made responsible for funding hospitals. At the same time that hospitals have tended to become more public in Canada, there has been a shift to more private for-profit ownership of the facilities that conduct laboratory and diagnostic testing to the point that the vast majority are now owned and operated by private corporations. In addition, there has been a trend toward private day surgery facilities for simpler, nonurgent types of surgeries. These private facilities mainly provide services under the terms of Medicare. 164 Glob Soc Welf (2016) 3:161–170 Physicians provide referrals for laboratory tests, X-ray, ad- vanced diagnostics, and day surgery procedures. Patients then obtain these services at a private clinic without a fee. The private facilities are reimbursed directly by provincial author- ities (in most provinces, by RHAs) for the tests. The few exceptions are a handful of private non-Medicare facilities or clinics concentrated mainly in Montreal, Calgary, and Vancouver. These private clinics serve mainly non- Medicare patients although there has been some controversy when Medicare patients have used these facilities to avoid public queues for elective (nonurgent) services and then have attempted to be reimbursed through the public system. In 1993–1994, for example, there was a major clash be- tween the federal government and the provincial government of Alberta over facility fees. Seven private eye surgery clinics, two private abortion clinics, and two magnetic resonance im- aging (MRI) centers began charging patients facility fees in clear contravention of the Canada Health Act. Federal Minister of Health Diane Marleau warned the government of Alberta that the provincial government that Alberta would be deducted the amount of these facility fees from its share of the federal health transfer if the practice continued.Marleau stated the basis of her concerns to the media: B... I’m deeply concerned...with trends that are developing toward a two-tier health system. Private clinics appear to run contrary to the spirit of the Canada Health Act. They do create a two-tier system, more accessible to the rich than to the poor^ (quoted in Bhatia and Coleman 2003, p. 733). Marleau was supported in her view by the provincial gov- ernments supporting the policy intent of the Canada Health Act. The most vocal of these was Saskatchewan’s social dem- ocratic premier, Roy Romanow who stated that the govern- ment members in Alberta were B…turning the clock as fast they can [on Canadian Medicare]. Their solutions are simplis- tic and they amount to one: punish the poor^ (quoted in Bhatia and Coleman 2003, p. 733–4). The government of Alberta refused to change its position and was subjected to a deduction of $420,000 a month, a relatively small amount but one that gained public attention and opposition in the province. Two years later, the government backed down in the face of do- mestic opposition and negotiated with the private clinics to drop its user charges to patients (Bhatia and Coleman 2003). In recent years, the debate concerning user charges by pri- vate clinics has become part of a larger legal debate concerning the Charter of Rights and Freedoms. In 2005, the Supreme Court of Canada decided that provincial governments would not be permitted to uphold legal prohibitions on private insur- ance for nonurgent Medicare services if waiting times were excessive (Flood 2007). Currently, there is a case before the courts in British Columbia where a private surgical clinic has argued that patients have a constitutional right of access to private surgical services because of what it considers excessive Medicare wait times for elective procedures. While there have been important changes in the delivery of hospital, laboratory, diagnostic, and day surgeryMedicare ser- vices, the one constant has been the private and independent position of physicians. As pointed out decades ago by R. David Naylor, universal medical care coverage was established as a public payment but private practice system in the 1960s, and it has remained the same ever since. Doctors have the status of independent contractors that the vast major- ity of physicians working in these public facilities remain private professionals. While RHAs (and LHINs in Ontario) are ostensibly responsible for ensuring the coordination and continuity of health care and therefore in charge of organizing services, provincial ministries of health remain responsible for paying the physicians who deliver those services, creating major challenges for the alignment of incentives (Grant and Hurley 2013; Romanow 2002). Indeed, the simple fact that the remuneration physicians receive for diagnosing and treating patients in RHA or private hospital facilities comes from provincial ministries of health means that they remain highly independent of the organiza- tions in which they conduct at least some of their work. While this private arrangement for hospital-based physicians is not unique to Canada, it is a rare arrangement. In the UK, for example, almost all hospital-based consultants (i.e., special- ists) are salaried and work for the government-owned hospi- tals called NHS trusts (Boyle 2011). While there have been no major comparative studies of the governance and payment of specialists in higher-income OECD countries, one recent study of six European countries found pronounced differences among the countries in terms of the percent of specialists exclusively self-employed, the per- cent exclusively salaried and the percent working as both con- tractors and employees. However, the spectrum ranged from 72% exclusively self-employed (Belgium) to 82% exclusive- ly salaried (Denmark) as of 2010 (Kok et al. 2015). In England where most specialists became salaried employees of a nation- al hospital system that was created with the introduction of the National Health Service (NHS) in 1948, only 4 % of special- ists are exclusively self-employed. In The Netherlands, a country that has had a long tradition of medical self-employ- ment, the figure is only 43 % (Kok et al. 2015). Although there is no definitive study on this subject in Canada, the limited evidence indicates that the vast majority of specialists are exclusively self-employed, well above the 72 % mark in Belgium. This means that Canadian specialists are likely at the very extreme end of the spectrum in terms of managing their affairs as private businesses. The one parallel may be Australia where there is a long history of physician independence and the majority of specialists are also self- employed. However, it is important to note that independent specialists in Australia and most European countries contract with the hospital organizations with which they work thereby establishing some direct accountability that is missing in the Glob Soc Welf (2016) 3:161–170 165 Canadian case (Grant and Hurley 2013; Healey et al. 2006; Schäfer et al. 2010). Moreover, there is no sustained movement or trend in Canada for RHAs or independent hospitals to hire specialists either through contract or salary. Instead, the vast majority of specialists receive remuneration directly from provincial min- istries through agreed-upon fee schedules or alternative pay- ment contracts and have little direct accountability relation- ship with the hospitals or RHAs within which they provide inpatient and outpatient care. The way in which federal and provincial governments have defined Bmedical care^ has meant that primary care and spe- cialist doctors have Bsecured a virtual monopoly over public sector payments for medical services and associated tests,^ a description of Medicare in Australia (Healey et al. 2006, p. 57), but one which applies equally well to Canada. Tuohy (1999) has described this arrangement as a duopoly between the pro- vincial governments as the sole payers ofMedicare and doctors as privileged provider of Medicare services. As a consequence, the vast majority Canadian doctors remain private practitioners to a greater proportion than most other OECD countries. In the Canadian case, this duopoly has resulted in long- standing compromises between provincial policy-makers and organized medicine on the rules of the game. On the one hand (in most provinces), physicians have the right to opt out of Medicare. The historic quid pro quo is that opted out physicians must truly opt out and must rely exclusively on non-Medicare patients who are prepared to pay directly or those patients referred for treatment through a separate social insurance stream of provincial workers’ compensation board (WCB) clients. At the same time, it is not the provincial gov- ernment but the doctors themselves, through their own pro- vincial self-regulatory organizations (the various provincial colleges of physicians and surgeons), who administer this ar- rangement by providing provincial Medicare billing numbers to those doctors working within theMedicare payment system and denying them to opted-out doctors. Holding everything else constant, any judicial decision al- tering these long-standing arrangements by creating new forms of access to private services for those who have the ability to pay or access private insurance is likely to have two results in the short run. The first likely consequence would be to reduce access to Medicare services for those less able to pay or access private insurance (due to risk factors such as age or preexisting conditions) by providing an incentive to physicians to focus on privately funded patients, a phenomenon common in countries such as Australia with dual practice (Duckett 2005). Institutional Long-Term Care There is a very limited literature on the policy evolution of institutional long-term care (LTC) in Canada. In particular, although it appears that provincial governments began to sub- sidize LTC in the 1970s for those in need, there has been no systematic comparison of provincial policies in this area. The means test applied by most provincial governments is that the provincial government provides for the clinical needs of high- needs patients including 24-h nursing care and supervision. However, LTC residents above a certain wealth or income threshold must pay for their own accommodation and living expenses in provincially approved LTC facilities. Due to data limitations, it is almost impossible to calculate in any precise way the public-private ratios for the financing of institutional LTC. Moreover, it has been complicated in recent years by the growth of private sector assisted living. With the growth in public waiting lists for approved (i.e., provincially subsidized) LTC facilities, the private sector has increasingly been providing high-needs care to residents able to pay the full cost of both accommodation and clinical care. Based on CIHI’s calculation for Bother institutions^—a cate- gory largely made up of facility-based long-term care (LTC) institutions—we know that the public-private ratio was rough- ly 70:30 based onCIHI’s forecast for 2015 (CIHI 2015b). This means that provincial government programs and subsidies for LTC are substantial in all provinces. Within the provincially regulated and subsidized LTC sec- tor, there is a variation in ownership across the country, but there has been a significant growth in the private for-profit sector since 2000 (McGregor and Ronald 2011). The largest private-for-profit market of provincially approved facilities is in Ontario, where over half of LTC beds are in for-profit fa- cilities, compared to 31 % in BC and only 8 % in Saskatchewan (in 2008) (McGregor and Ronald 2011). The implication of this shift in ownership over time for lower- income Canadians requires further investigation; although given the evidence suggesting poorer quality of care among for-profit compared to public or not-for-profit facilities, this trend has raised concerns about the overall quality of facility- based care in Canada (McGregor and Ronald 2011). A recent analysis of long-term care policies in three Canadian provinces also documented increasing private sector involvement in long-term care in two of the provinces (Alberta and Ontario) in part in response to health care budget constraints (in particular in the 1990s) and to address short- ages of long-term care facilities by partnering with the private sector (Palley 2013). These shortages persist and are evi- denced by lengthy wait lists to enter facilities. For example, in Ontario, the median wait to enter a LTC facility among nonurgent community-dwelling individuals was 68 days in 2004/05 compared to 109 days in 2014/15 (Health Quality Ontario 2016). At the same time, the publicly subsidized home and community care services are limited; there is a heavily reliance on both informal caregivers and private sector providers that are paid out of pocket (Palley 2013; Williams et al. 2016). Therefore, lower-incomeCanadians face financial 166 Glob Soc Welf (2016) 3:161–170 barriers to home and community services beyond the limited publicly funded services for which they are deemed to be entitled. Moreover, they may also face high costs of institu- tional care in some provinces, like Alberta, where accommo- dation and nonmedical expenses are not regulated (Palley 2013). In Ontario, the limited supply of personal care within publicly funded facilities has led to an increasing reliance on private caregivers to fill the gap for only those able to pay (Daly et al. 2015). To our knowledge, only one study to date has measured income-related inequalities in access to long-term care facili- ties in Canada (Um 2016). This study reviewed publicly avail- able wait list information for each publicly funded long-term care facility in Toronto, Canada’s largest city, and found wait times for basic accommodation (rooms with two to four beds) were about 3 months longer on average than those for private accommodation. The implication of this discrepancy is that people who can afford to pay the higher cost of private ac- commodation ($2535.23 vs. $1774.81 CAD monthly) face much shorter waits than those with lower income (Um 2016). Prescription Drugs One of the most criticized dimensions of Medicare is that, unlike most other high-income industrialized countries, UHC in Canada excludes pharmaceuticals unless provided as part of inpatient care within a hospital. Historically, this lack of public coverage posed a major financial barrier to access to needed outpatient prescription drug therapies. Private health insurance covering prescription drugs, dental care, and vision care has long been part of employment- based benefit packages in Canada, so a significant number of Canadians in corporate, unionized, and professional envi- ronments are covered. However, this created a gap in coverage for those in low-paid employment, temporary or seasonal work, retired persons, and the unemployed. In the 1970s, provincial governments began addressing this gap by creating provincial drug plans that targeted the very poor—generally defined as those individuals receiving social assistance—and older adults above retirement age (defined as 65 and older) and therefore no longer receiving employment benefits. The current design of most provincial drug plans continues to reflect this original policy purpose. For its part, the federal government filled a similar gap for Indigenous people living on reserves and Inuit living in the far north through a program known as Non-Insured Health Benefits (NIHB). Among the poorest and most marginalized citizens of Canada, most NIHB beneficiaries were not part of the for- mal economy and therefore unable to benefit from drug (or dental) coverage under employment-based private health in- surance plans. Of the forecasted $29.2 billion spent on prescription drugs in Canada in 2015, governments in Canada were responsible for financing $12.6 billion (43 %) of prescription drug thera- pies through public drug coverage and drug subsidy plans. The remaining amount (57 %) is financed through private health insurance (35 %), most often through employment ben- efit plans, and 22 % out of pocket payment (CIHI 2015b). However, in part, a consequence of these governments having limited regulatory control of prescription drug pricing and the power of pharmaceutical companies and interest groups in influencing the drugs included in provincial formu- laries in a fragmented policy environment, the current pro- grams have grown rapidly in cost since at least the mid- 1970s (CIHI 2015b; Morgan et al. 2013). To address this inefficiency as well as improve access, the evidence points away from the status quo of public and private insurance ar- rangements to a single-payer public system administered in ways that parallel Medicare in Canada. However, difficult changes in governance and administration are required to achieve lower cost and universal coverage, and despite the fiscal and equity arguments in favor of major reform, govern- ment initiative at federal or provincial levels has remained limited (Morgan et al. 2015a, b). The impact of the exclusion of prescription drugs outside from UHC on low-income Canadians is apparent. Among Canadians who receive a prescription, 1 in 10 report cost- related nonadherence, the odds of which significantly increase for lower-income Canadians and those without prescription drug insurance (Law et a l . 2012) . In the 2008 Commonwealth Fund survey of people with chronic condi- tions, 22% of Canadians with below-average income reported not to fill a prescription or to skip doses because of costs in the past 2 years, compared to less than 15 % of people with below average income in France (14 %), the UK (10 %), and the Netherlands (4 %) (Schoen et al. 2008). The impact of not holding prescription coverage, which disproportionately affects lower-income Canadians, is not on- ly to use less needed medications for chronic conditions (e.g., in a study from Ontario; Kratzer et al. 2015) but also to reduce the likelihood of seeking primary physician care when needed (Allin and Hurley 2009). Moreover, even among those with public coverage through a provincial prescription drug pro- gram, the user charges and deductibles that are in place have the effect of deterring use among people with lower income (as evidenced in Quebec for example, Tamblyn et al. 2001, and Ontario, Allin et al. 2013). Dental Care Canada has among the most private systems of dental care relative to the high-income welfare states listed in Table 2. Approximately 95 % of dental services are financed privately, Glob Soc Welf (2016) 3:161–170 167 either through employment-based private health insurance or out-of-pocket payments. Private dental practitioners are re- sponsible for the delivery of almost all dental services in Canada. Low-income Canadians have consistently faced con- siderable financial barriers to access to both preventive and curative dental care. The policy response to this challenge has been twofold. The first and most pronounced response has been to extend coverage to those receiving provincial social assistance (wel- fare). However, in most provinces, it is up to private dental practitioners to decide whether to accept social assistance cli- ents at reimbursement rates set by provincial governments. Such interventions did not, and still do not, include the work- ing poor. The federal government in turn has provided cover- age for eligible First Nation individuals and Inuit under the NIHB program discussed earlier. Originally, this was a re- sponse to a situation where most NIHB beneficiaries did not have access to employment-based private health insurance. The second policy approach was to target school-aged chil- dren in prevention and treatment programs directly delivered by provincial governments through paraprofessionals. However, the dental profession and governments with more conservative and market ideologies have consistently opposed this bolder policy approach (Mathu-Muju et al. 2013). As a consequence, only two provincial governments have attempted to establish such programs. In the 1970s, the Saskatchewan government implemented a program covering the entire population while the Manitoba government established a smaller program targeting rural residents. Both programs were implemented by social democratic govern- ments and were subsequently terminated by more conservative-leaning governments in the 1980s. From its implementation in 1974 until its dismantlement in 1987, the Saskatchewan Dental Plan (SDP) provided a range of dental prevention and treatment services to hundreds of thousands of school children throughout the province of Saskatchewan in Canada. Dental therapists served a total pro- vincial population of just slightly less than 1 million residents distributed in a vast geographical area (651,036 km2) consid- erably larger than the state of California. At its peak, the SDP had 150 dental therapists providing preventive and curative dental therapy to 90 % of enrolled school children in Saskatchewan (Nash et al. 2008). Although a universal pro- gram, the SDP provided access to a generation of children from low-income families and changed the trajectory of oral health outcomes in the province. In Canada, there have been smaller-scale initiatives in other provinces and the northern territories, but these programs have not been universal in nature and were generally based on a fee- for-service (FFS) private practice model (Wolfson 1997). These programs targeted subpopulations based upon income, location, or beneficiary status as Bregistered Indians^ and el- igible Inuit under the NIHB as discussed above. The only policy intervention similar to the SDP was in Manitoba where the provincial government established the school-based Manitoba Children’s Dental Program, the range of which was limited to targeted rural areas. This program operated from 1976 to 1993 when it too was eliminated after years of opposition by organized dentistry in the province (Nash et al. 2008). Indeed, the Canadian Dental Association and provin- cial dental associations consistently opposed this public policy alternative to the private practice model in all provinces. Given the exclusion of dental care from UHC, it is not surprising that there is consistent evidence of inequity by in- come in the use of dental services. The 2007 Commonwealth Fund international survey found 33 % of Canadians with below-average income who needed dental care did not see a dentist because of cost which was lower than in Australia, New Zealand, and the USA but significantly higher than in Germany, the Netherlands, and the UK (Schoen et al. 2007). A significant pro-rich bias in dental care is evident across the 18 OECD countries studied, and the magnitude of inequity was higher in Canada than all other countries except the USA (Devaux 2016). In Canada, inequity by income appears to be highest for preventive dental care (Grignon et al. 2010). Given private insurance for dental care is mostly held by higher-income Canadians (Bhatti et al. 2007), the variations in coverage across provinces in part explain the variations in the extent of income-related inequalities in dental care that is observed (Allin 2008). Conclusion To better understand the nature of the public-private modes of service delivery, the highly decentralized Canadian health sys- tem is subdivided into three layers based on the nature of government stewardship in the federation and the degree of state of intervention. First, there is Medicare, which embraces universally accessible hospital and physician services fi- nanced and regulated by federal and provincial orders of gov- ernment. Second, there are the mixed services—prescription drugs and long-term care—subject to some state intervention through targeted coverage policies which address gaps not filled by the private sector. Finally, there are the private ser- vices (e.g., dental care), which are almost entirely financing and delivered privately. Each of these three layers was exam- ined separately in order to minimize confusion and gain great- er analytical clarity. Although Medicare is the most public layer of the Canadian health system, universal health coverage nonethe- less presents some equity conundrums. In spite of physician services being free at the point of use, income-related inequal- ities favoring the rich appear to be significant in Canada, and inequalities are actually larger in Canada than in other high- income countries with universal health coverage. In part, the 168 Glob Soc Welf (2016) 3:161–170 inequalities in access to a GP relate to prescription drug ther- apies (excluded from UHC) that often result from a physician visit. In addition, pro-rich specialist access could be due to inequitable referral patterns by GPs favoring higher-income and higher-educated individuals who are better able to advo- cate for themselves. Illness prevention programs also present some challenges. For example, there is a strong pro-rich bias in cancer screening due to the tendency for higher-income individuals to take advantage of such policies and programs. There is also a growing trend toward private sector involve- ment in Medicare in terms of the delivery laboratory, ad- vanced diagnostic and ambulatory surgical services. When forced to comply with the standards set by the Canada Health Act as well as provincial rules and regulations protecting Medicare, these private services have not posed a major challenge to equity. However, when coupled with the ability to jump public queues and user fees, these private ser- vices can create a two tier system which ultimately delivers less timely and lower quality services to low-income Canadians. When it comes to long-term care and prescription drugs, Canadians live in a two tier world. Supply constraints for publicly financed and delivered facility-based LTC mean that Canadians with significant income can bypass the public sys- tem by paying privately for private sector facilities which have increasingly moved into higher needs care. Moreover, there seems to be little planning or effort by provincial governments to address the growing shortages of publicly subsidized LTC facilities. In systems where the sector is heavily regulated (e.g., in Quebec and Ontario), the publicly funded system is accessible, but the supply remains very constrained. As a re- sult, individuals who can afford to do so pay privately for additional needed services, or to opt out of the public system in order to bypass wait lists. The case of prescription drugs is similar. Provincial drug plans are meant to fill in the gaps left by employment-based private health insurance, but the public plans impose financial barriers through user charges. This policy negatively affects access for the working poor and retirees. Fortunately, the poorest of the poor—individuals receiving social assis- tance—are generally exempt from such user fees. There has been a pronounced trend in all provinces to provide cata- strophic drug coverage. However, these policies leave in place financial barriers to access that disproportionately affect poorer Canadians, which in turn lead to nonadherence and related adverse health outcomes. Dental care is an almost exclusively private. As a conse- quence, inequalities in use of dental care services are larger in Canada than in all other high-income countries except the USA. Most dental insurance is employment-based and con- centrated in higher salaried occupational groups. Since there is little government intervention to provide services and almost no subsidization of dental insurance (except for targeted groups such as eligible First Nation individuals and Inuit), the result is much poorer oral health results for poorer Canadians. Even Medicare, the most public layer of Canadian health care based on stewardship, financing, and administration, has always had a large component of private delivery. However, the introduction of private delivery for medically necessary services operating outside the regulatory framework of Medicare—such as what has occurred with advanced diagnos- tics and ambulatory surgical services—could Bstretch^ the availability of scarce human resources and create inequities in terms of access. However, this still poses less of an issue than the longstanding inequities found in the mixed and pri- vate layers of health care in Canada. The lack of pharmaceu- tical coverage and dental care coverage, as well as the costs and availability of institutional (and noninstitutional) long- term care, present major equity and access issues for many lower-income Canadians. Such issues occur in areas where the presence of a fee-for-service clinical practice and the high degree of private sector Bmarketization^ are significant factors with regard to the delivery of healthcare services. References Allin, S. (2008). Does equity in healthcare use vary across Canadian provinces? Healthcare Policy, 3(4), 83–99. Allin, S., & Hurley, J. (2009). Inequity in publicly funded physician care: what is the role of private prescription drug insurance? Health Economics, 18(10), 1218–1232. Allin, S., Law, M., & Laporte, A. (2013). How does complementary private prescription drug insurance coverage affect seniors’ use of publicly funded medication? Health Policy, 110(2–3), 147–155. Asada, Y., & Kephart, G. (2007). Equity in health services use and inten- sity of use in Canada. BMC Health Services Research, 7, 41. Bhatia, V., & Coleman, W. D. (2003). Ideas and discourse: reform and resistance in the Canadian and German health systems. Canadian Journal of Political Science, 36(4), 715–739. Bhatti, T., Rana, Z., & Grootendorst, P. (2007). Dental insurance, income and the use of dental care in Canada. Journal of the Canadian Dental Association, 73(1), 57a–57h. Boyle, S. (2011). United Kingdom (England): Health system review. Health Systems in Transition, 13(1):1–486. CIHI (2015a). Your Health System. Canadian Institute for Health Information. Retrieved from www.yourhealthsystem.cihi.ca CIHI (2015b). National Health Expenditure Trends, 1975 to 2015. Ottawa: Canadian Institute for Health Information. Daly, T., Armstrong, P., & Lowndes, R. (2015). Liminality in Ontario’s long-term care facilities: private companions’ care work in the space ‘betwixt and between’. Competition & Change, 19(3), 246–264. Deber, R. B. (2004). Delivering health care services: public, not-for-prof- it, or private? In G. P. Marchildon, T. McIntosh, & P.-G. Forest (Eds.), The fiscal sustainability of health care in Canada: Romanow papers (Vol. 1, pp. 233–296). Toronto: University of Toronto Press. Glob Soc Welf (2016) 3:161–170 169 http://www.yourhealthsystem.cihi.ca Devaux, M. (2016). Income-related inequalities and inequities in health care services utilisation in 18 selected OECD countries. European Journal of Health Economics, 16, 21–23. Duckett, S. (2005). Living in the parallel universe in Australia: public medicare and private hospitals. CMAJ, 173, 745–747. Dunlop, P. C., Coyte, P. C., &McIsaac,W. (2000). Socio-economic status and the utilization of physicians’ services: results from the Canadian National Population Health Survey. Social Science and Medicine, 51(1), 123–133. Flood, C. (2007) Chaoulli’s legacy for the future of Canadian health care policy. In B. Campbell & G. Marchildon (Eds.), Medicare: Facts, myths, problems, promise (pp.156–191). Toronto: James Lorimer & Company. Flood, C., & Archibald, T. (2001). The illegality of private health care in Canada. CMAJ, 164(4), 825–830. Flood, C., & Haugen, A. (2010). Is Canada odd? A comparison of European and Canadian approaches to choices and regulation of the public/private divide in health care. Health Economics, Policy and Law, 5(3), 319–341. Grant, H.M., &Hurley, J. (2013).Unhealthy pressure: how physician pay demands put the squeeze on provincial health-care budgets. Calgary: SPP Research Papers, University of Calgary School of Public Policy. Grignon, M., Hurley, J., Wang, L., & Allin, S. (2010). Inequity in a market-based health system: evidence from Canada’s dental sector. Health Policy, 98(1), 81–90. Glied, S. (2008). Health care financing, efficiency and equity. NBER Working Paper No. 13881. Boston: National Bureau of Economic Research. Healey, J., Sharman, E., & Lokuge, B. (2006). Australia: health system review. Health Systems in Transition, 8(5), 1–158. Health Quality Ontario (2016). System performance: waiting for place- ment. Retrieved from http://www.hqontario.ca/System- Performance/Long-Term-Care-Sector-Performance/Quality- Indicators/Waiting-for-Placement Kok, L., Boyle, S., Lammers, M., & Tempelman, C. (2015). Remuneration of medical specialists: drivers of the differences be- tween six European countries. Health Policy, 119(9), 1188–1196. Kratzer, J., Cheng, L., Allin, S., & Law, M. R. (2015). The impact of private insurance coverage on prescription drug use in Ontario, Canada. Healthcare Policy, 10(4), 62–74. Law, M.R., Cheng, L,, Dhalla, I.A., Heard, D., Morgan, S. (2012). The effect of cost on adherence to prescription medications in Canada. CMAJ, 184(3), 297–302. Maddison, A.R., Asada, Y., and Urquhart, R. (2011). Inequity in access to cancer care: a review of the Canadian literature. Cancer Causes & Control, 22(3) 359–366. Marchildon, G. P. (2004). The public/private debate in the funding, ad- ministration and delivery of healthcare in Canada. Healthcare Papers, 4(4), 61–68. Marchildon, G. P. (2012). Canadian Medicare: why history matters. In G. P. Marchildon (Ed.), Making Medicare: new perspectives on the history of Medicare in Canada (pp. 3–18). Toronto: University of Toronto Press. Marchildon, G. P. (2013).Health systems in transition: Canada (2nd ed.). Toronto: University of Toronto Press. Marchildon, G. P. (2014). The three dimensions of universal Medicare in Canada. Canadian Public Administration, 57(3), 363–382. Mathu-Muju, K. R., Friedman, J. W., & Nash, D. A. (2013). Oral health care for children in countries using dental therapists in public, school-based programs, contrasted with that of the United States, using dentists in a private practice model. American Journal of Public Health, 103(9), e7–e13. McGrail, K. (2008). Income-related inequities: cross-sectional analyses of the use of medicare services in British Columbia in 1992 and 2002. Open Medicine, 2(4), e91–e98. McGregor, M. J., and Ronald, L. A. (2011). Residential long-term care for Canadian seniors: nonprofit, for-profit or does it matter? IRPP Study No. 14. Montreal: Institute for Research on Public Policy. Morgan, S.G., Daw, J. & Law, M. (2013). Rethinking Pharmacare in Canada. C.D. Howe Institute Commentary 384, Toronto: C.D. Howe Institute. Morgan, S.G., Law, M., Daw, J., Abraham, L. & Martin, D. (2015a). Estimated cost of universal public coverage of prescription drugs in Canada CMAJ online March 16, 2015, doi 10.1503/cmaj.141564 Morgan, S.G., Martin, D., Gagnon, M.-A., Mintzes, B. & Daw, J. (2015b). The future of drug coverage in Canada. Vancouver: Pharmaceutical Policy Research Collaborative, University of British Columbia. Nash, D. A., Friedman, J. W., Kardos, T. B., Kardos, R. L., Schwarz, E., et al. (2008). Dental therapists: a global perspective. International Dental Journal, 58(2), 61–70. Naylor, C. D. (1986). Private practice, public payment: Canadian med- icine and the politics of health insurance, 1911–1966. Montreal: McGill-Queen’s University Press. OECD (2015). OECD Health Statistics 2015. Paris: Organisation for Economic Co-operation and Development. Online database re- trieved from: http://stats.oecd.org Palley, H. (2013). Long-term care service policies in three Canadian provinces: Alberta, Quebec and Ontario–examining the national and subnational contexts. International Journal of Canadian Studies, 47, 57–85. Romanow, R.J. (2002). Building on values: the future of health care in Canada. Ottawa: Commission on the Future of Health Care in Canada. Schäfer, W., Kroneman, M., Boerma, W., van den Berg, M., Westert, G., Devillé, W., et al. (2010). The Netherlands: health system review. Health Systems in Transition, 12(1), 1–229. Schoen, C., Osborn, R., Doty, M. M., Bishop, M., Peugh, J., & Murukutla, N. (2007). Toward higher-performance health systems: adults’ health care experiences in seven countries, 2007. Health Affairs, 26(6), w717–w734. Schoen, C., Osborn, R., How, S. K. H., Doty, M. M., & Peugh, J. (2008). In chronic condition: experiences of patients with complex health care needs, in eight countries, 2008. Health Affairs, 28(1), w1–w16. Tamblyn, R., Laprise, R., Hanley, J. A., Abrahamowicz, M., Scott, S., Mayo, N., et al. (2001). Adverse events associated with prescription drug cost-sharing among poor and elderly persons. Journal of the American Medical Association, 285(4), 421–429. Taylor, M. G. (1987). Health insurance and Canadian public policy: the seven decisions that created the Canadian health insurance system and their outcomes. Montreal: McGill-Queen’s University Press. Tuohy, C. H. (1999). Accidental logics: the dynamics of change in the health care arena in the United States, Britain and Canada. New York: Oxford University Press. Um, S. (2016). The cost of waiting for care: delivering equitable long- term care for Toronto’s diverse population. Toronto: Wellesley Institute. van Doorslaer, E., & Masseria, C. (2004). Income-related inequality in the use of medical care in 21 OECD countries. Paris: Organisation for Economic Co-operation and Development. Williams, P., Lum, J., Morton-Chang, F., Kuluski, K., Peckham, A., Warrick, N. & Ying, A. (2016) Integrating long-term care into a community-based continuum: shifting from Bbeds^ to Bplaces^. IRPP study no. 59, Montreal: Institute for Research on Public Policy. Wolfson, S. (1997). Use of paraprofessionals: the Saskatchewan dental plan. In E. D. Glor (Ed.), Policy innovation in the Saskatchewan public service, 1971–82 (pp. 126–139). Toronto: Captus Press. 170 Glob Soc Welf (2016) 3:161–170 http://www.hqontario.ca/System-Performance/Long-Term-Care-Sector-Performance/Quality-Indicators/Waiting-for-Placement http://www.hqontario.ca/System-Performance/Long-Term-Care-Sector-Performance/Quality-Indicators/Waiting-for-Placement http://www.hqontario.ca/System-Performance/Long-Term-Care-Sector-Performance/Quality-Indicators/Waiting-for-Placement http://dx.doi.org/10.1503/cmaj.141564 http://stats.oecd.org The Public-Private Mix in the Delivery of Health-Care Services: Its Relevance for Lower-Income Canadians Introduction: an Overall Health System Overview The Delivery of Canadian Medicare Services Trends in Terms of the Public or Private Delivery of Medicare Institutional Long-Term Care Prescription Drugs Dental Care Conclusion References
PURPOSEFUL READING (3-2-1) REPORT Version 2.0 Lightly Adapted from a template by Geraldine Van Gyn. Question 1: In your own words, what are the 3 most important concepts, ideas or issues in the reading? Briefly explain why you chose them. Concept 1 (In your own words) (2 marks) Concept 2 (In your own words) (2 marks) Concept 3 (In your own words) (2 marks) Question 2: What are 2 concepts, ideas or issues in the article that you had difficulty understanding, or that are missing but should have been included? In your own words, briefly explain what you did to correct the situation (e.g. looked up an unfamiliar word or a missing fact), and the result. Cite any sites or sources used in APA format. Issue 1 (In your own words) (1 mark) Citation 1 (in APA format) (1 mark) Issue 2 (In your own words) (1 mark) Citation 2 (in APA format) (1 mark) Question 3: What is the main economic story of the reading? (Economics studies the allocation of scarce resources.) Story (In your own words) (2 marks)

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