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PURPOSE Primary care access is a key health system metric, but little research has compared models to provide primary care access when one's regular physician is not available. We compared health system use after a visit with a patient's own family physician group (ie, within-group physician who was not the patient's primary physician) vs a visit with a walk-in clinic physician who was not part of the patient's family physician group. METHODS We conducted a population-based, retrospective cohort study using administrative data from Ontario, Canada, including all individuals formally enrolled with a family physician, from April 1, 2019 to March 31, 2020. We compared those visiting within-group physicians to those visiting walk-in clinic physicians using propensity score matching to account for differences in patient characteristics. The primary outcome was any emergency department visit within 7 days of the initial visit. RESULTS Matched patients who visited a within-group physician (N = 506,033) were 10% less likely to visit an emergency department in the 7 days after the initial visit compared to patients who saw a walk-in clinic physician (N = 506,033; 20,117 [4.0%] vs 22,320 [4.4%]; risk difference [RD] 0.4%; 95% CI 0.4-0.5; relative risk [RR] 0.90; 95% CI, 0.89-0.92). Restricting to visits occurring on weekends, the observed association was stronger (7,964 [3.7%] vs 10,055 [4.7%]; RD 1.0%; 95% CI 0.9-1.1; RR 0.79; 95% CI, 0.77-0.82). Those accessing after-hours within-group physician visits were more likely to have >1 additional virtual or in-person within-group physician visit within 7 days (virtual RR 1.86, in-person RR 1.87). CONCLUSIONS Compared to visiting a walk-in clinic physician, seeing a within- group physician after hours might decrease downstream emergency department visits. This finding could be explained by better continuity of care and can inform primary care service models and the policies that support them., INTRODUCTION Balancing timely access with care continuity remains an important challenge in primary care. Although 90% of the population in Ontario, Canada's most populous province, report having a family physician [...]

Investigating the mechanisms of behavior change interventions provides a more fulsome understanding of how and why interventions work (or don't work). We assessed mechanisms of two interventions (mailouts alone, and mailouts plus telephone support, informed by the Health Action Process Approach (HAPA) and Habit Theory), designed to increase medication adherence after myocardial infarction. We conducted a process evaluation alongside a pragmatic trial. Medication adherence was assessed via self-report at 12-months in the trial, and participants in all trial groups were invited to contemporaneously complete an additional questionnaire assessing targeted mechanisms (HAPA constructs and automaticity). We used multiple regression-based mediation models to investigate indirect effects. Of 589 respondents, 497 were analyzed (92 excluded due to missing data). Mailouts plus telephone support had statistically significant but small effects on intention, social support, action planning, coping planning, and automaticity. There were no indirect effects of interventions on medication adherence via these constructs. Therefore, while this intervention led to changes in proposed mechanisms, these changes were not great enough to lead to behavior change. Refinements (and subsequent evaluation) of the interventions are warranted, and our findings indicate that this could involve offering more intensive support to form plans and identify cues for taking medications, in addition to providing physical supports to encourage self-monitoring, feedback, and habit formation. Trial registration: ClinicalTrials.gov: NCT02382731., Author(s): Nicola McCleary [sup.1] [sup.2] , Noah M. Ivers [sup.3] [sup.4] [sup.5] [sup.6] [sup.7] [sup.8] , J-D Schwalm [sup.9] [sup.10] , Holly O. Witteman [sup.1] [sup.11] , Monica Taljaard [sup.1] [...]

Objective: We aimed to better understand the challenges related to type 2 diabetes medication-taking through Theoretical Domains Framework (TDF)-guided interviews with people with type 2 diabetes with varying degrees of medication-taking. Methods: One-on-one qualitative interviews following a semistructured discussion guide informed by the TDF were conducted. Thirty people with type 2 diabetes in Canada were interviewed, with representation from across the country, of both sexes (47% female), of people with various diabetes durations (mean 12.9 ± 7.9 years), with different types of medication plans (n = 15 on polypharmacy), and with various medication-taking levels (n = 10 each for low-, medium-, and high-engagement groups). Results: Themes related to medication-taking from interviews mapped to 12 of the 14 TDF theme domains, with the exclusion of the knowledge and skills domains. The most prominent domains, as determined by high-frequency themes or themes for which people with low and high medication-taking had contrasting perspectives, were 1) emotion; 2) memory, attention, and decision processes; 3) behavioral regulation; 4) beliefs about consequences; 5) goals; and 6) environmental context and resources. Conclusion: Through our interviews, several areas of focus emerged that may help efforts to increase medication-taking. To validate these findings, future quantitative research is warranted to help support people with type 2 diabetes in overcoming psychological and behavioral barriers to medication-taking. [ABSTRACT FROM AUTHOR]

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data. [ABSTRACT FROM AUTHOR]