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Background: Adult day programs provide critical supports to older adults and their family or friend caregivers. High-quality care in the community for as long as possible and minimizing facility-based continuing care are key priorities of older adults, their caregivers, and health care systems. While most older adults in need of care live in the community, about 10% of newly admitted care home residents have relatively low care needs that could be met in the community with the right supports. However, research on the effects of day programs is inconsistent. The methodological quality of studies is poor, and we especially lack robust, longitudinal research., Objective: Our research objectives are to (1) compare patterns of day program use (including nonuse) by province (Alberta, British Columbia, and Manitoba) and time; (2) compare characteristics of older adults by day program use pattern (including nonuse), province, and time; and (3) assess effects of day programs on attendees, compared with a propensity score-matched cohort of older nonattendees in the community., Methods: In this population-based retrospective cohort study, we will use clinical and health administrative data of older adults (65+ years of age) who received publicly funded continuing care in the community in the Canadian provinces of Alberta, British Columbia, and Manitoba between January 1, 2012, and December 31, 2024. We will compare patterns of day program use between provinces and assess changes over time. We will then compare characteristics of older adults (eg, age, sex, physical or cognitive disability, area-based deprivation indices, and caregiver availability or distress) by pattern of day program use or nonuse, province, and time. Finally, we will create a propensity score-matched comparison group of older adults in the community, who have not attended a day program. Using time-to-event models and general estimating equations, we will assess whether day program attendees compared with nonattendees enter care homes later; use emergency, acute, or primary care less frequently; experience less cognitive and physical decline; and have better mental health., Results: This will be a 3-year study (July 1, 2024, to June 30, 2027). We received ethics approvals from the relevant ethics boards. Starting on July 1, 2024, we will work with the 3 provincial health systems on data access and linkage, and we expect data analyses to start in early 2025., Conclusions: This study will generate robust Canadian evidence on the question whether day programs have positive, negative, or no effects on various older adult and caregiver outcomes. This will be a prerequisite to improving the quality of care provided to older adults in day programs, ultimately improving the quality of life of older adults and their caregivers., Trial Registration: ClinicalTrials.gov NCT06440447; https://clinicaltrials.gov/study/NCT06440447., International Registered Report Identifier (irrid): PRR1-10.2196/60896., (©Matthias Hoben, Colleen J Maxwell, Andrea Ubell, Malcolm B Doupe, Zahra Goodarzi, Saleema Allana, Ron Beleno, Whitney Berta, Jennifer Bethell, Tamara Daly, Liane Ginsburg, Atiqur SM - Rahman, Hung Nguyen, Kaitlyn Tate, Kimberlyn McGrail. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 30.08.2024.)

Objectives: To assess the experiences and perceived impacts of the Aging, Community and Health Research Unit-Community Partnership Program (ACHRU-CPP) from the perspectives of older adults with diabetes and other chronic conditions. The ACHRU-CPP is a complex 6-month self-management evidence-based intervention for community-living older adults aged 65 years or older with type 1 or type 2 diabetes and at least one other chronic condition. It includes home and phone visits, care coordination, system navigation support, caregiver support and group wellness sessions delivered by a nurse, dietitian or nutritionist, and community programme coordinator., Design: Qualitative descriptive design embedded within a randomised controlled trial was used., Setting: Six trial sites offering primary care services from three Canadian provinces (ie, Ontario, Quebec and Prince Edward Island) were included., Participants: The sample was 45 community-living older adults aged 65 years or older with diabetes and at least one other chronic condition., Methods: Participants completed semistructured postintervention interviews by phone in English or French. The analytical process followed Braun and Clarke's experiential thematic analysis framework. Patient partners informed study design and interpretation., Results: The mean age of older adults was 71.7 years, and the mean length of time living with diabetes was 18.8 years. Older adults reported positive experiences with the ACHRU-CPP that supported diabetes self-management, such as improved knowledge in managing diabetes and other chronic conditions, enhanced physical activity and function, improved eating habits, and opportunities for socialisation. They reported being connected to community resources by the intervention team to address social determinants of health and support self-management., Conclusions: Older adults perceived that a 6-month person-centred intervention collaboratively delivered by a team of health and social care providers helped support chronic disease self-management. There is a need for providers to help older adults connect with available health and social services in the community., Trial Registration Number: ClinicalTrials.gov ID: NCT03664583; Results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Unpaid caregiving, care provided by family/friends, is a public health issue of increasing importance. COVID-19 worsened the mental health conditions of unpaid caregivers, increasing substance/drug use and early development of chronic disease. The impact of the intersections of race and ethnicity, sex, age and gender along with unpaid care work and caregivers' health and well-being is unknown. The aim of this study is to describe the inequities of caregiver well-being across the intersections of race and ethnicity, sex, age and gender using a cross-sectional survey design., Methods and Analysis: We are collaborating with unpaid caregivers and community organisations to recruit a non-probability sample of unpaid caregivers over 18 years of age (n=525). Recruitment will focus on a target sample of 305 South Asian, Chinese and Black people living in Canada, who represent 60% of the Canadian racial and ethnic populations. The following surveys will be combined into one survey: Participant Demographic Form, Caregiver Well-Being Index, interRAI Self-report of Carer Needs and the GENESIS (GENdEr and Sex DetermInantS of Cardiovascular Disease: From Bench to Beyond-Premature Acute Coronary Syndrome) PRAXY Questionnaire. Sample characteristics will be summarised using descriptive statistics. The scores from the Caregiver Well-Being Index will be dichotomised into fair/poor and good/excellent. A two-stage analytical strategy will be undertaken using logistic regression to model fair/poor well-being and good/excellent well-being according to the following axes of difference set a priori: sex, race and ethnicity, gender identity, age, gender relations, gender roles and institutionalised gender. The first stage of analysis will model the main effects of each factor and in the second stage of analysis, interaction terms will be added to each model., Ethics and Dissemination: The University of Toronto's Health Sciences Research Ethics Board granted approval on 9 August 2022 (protocol number: 42609). Knowledge will be disseminated in pamphlets/infographics/email listservs/newsletters and journal articles, conference presentation and public forums, social media and through the study website., Trial Registration Number: This is registered in the Open Sciences Framework with a Registration DOI as follows: https://doi.org/10.17605/OSF.IO/PB9TD., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders., Methods: This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews., Results: Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality-denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don't Matter: Just Another Tool in the Toolbox- reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword-the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care-broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care-elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation., Conclusion: The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care., (© 2022. The Author(s).)

Background. In Canada, caregivers of older adults receiving home care face difficult decisions that may lead to decision regret. We assessed difficult decisions and decision regret among caregivers of older adults receiving home care services and factors associated with decision regret. Methods. From March 13 to 30, 2020, at the outbreak of the COVID-19 pandemic, we conducted an online survey with caregivers of older adults receiving home care in the 10 Canadian provinces. We distributed a self-administered questionnaire through Canada's largest and most representative private online panel. We identified types of difficult health-related decisions faced in the past year and their frequency and evaluated decision regret using the Decision Regret Scale (DRS), scored from 0 to 100. We performed descriptive statistics as well as bivariable and multivariable linear regression to identify factors predicting decision regret. Results. Among 932 participants, the mean age was 42.2 y (SD = 15.6 y), and 58.4% were male. The most frequently reported difficult decisions were regarding housing and safety (75.1%). The mean DRS score was 28.8/100 (SD = 8.6). Factors associated with less decision regret included higher caregiver age, involvement of other family members in the decision-making process, wanting to receive information about the options, and considering organizations interested in the decision topic and health care professionals as trustworthy sources of information (all P < 0.001). Factors associated with more decision regret included mismatch between the caregiver's preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care (all P < 0.001). Discussion. Decisions about housing and safety were the difficult decisions most frequently encountered by caregivers of older adults in this survey. Our results will inform future decision support interventions., Highlights: This is one of the first studies to assess decision regret among caregivers of older adults receiving home and community care services and to identify their most frequent difficult decisions.Difficult decisions were most frequently about housing and safety. Most caregivers of older adults in all 10 provinces of Canada experienced decision regret.Factors associated with less decision regret included higher caregiver age, the involvement of other family members in the decision-making process, wanting to receive information about the options, considering organizations interested in the decision topic, and health care professionals as trustworthy sources of information. Factors associated with more decision regret included mismatch between the caregiver's preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided entirely by a Foundation Grant (No. FDN-159937) from the Canadian Institutes of Health Research (CIHR). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. FL holds a Tier 1 Canada Research Chair in Shared Decision Making and Knowledge Translation. LPR holds a Tier 1 Canada Research Chair in Statistical Sampling and Data Analysis. AG is funded by a CIHR Patient-Oriented Research fellowship. The funders are not involved in the project., (© The Author(s) 2022.)

Context: The effective deployment of artificial intelligence (AI) in primary health care requires a match between the AI tools that are being developed and the needs of primary health care practitioners and patients. Currently, the majority of AI development targeted toward potential application in primary care is being conducted without the involvement of these stakeholders. Objective: To identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. Study Design: A descriptive qualitative approach was taken in this study. Fourteen in-depth interviews were conducted with primary care and digital health stakeholders. Setting: Province of Ontario, Canada Population studied: Primary health care and digital health stakeholders Outcome Measures: N/A Results: Two main themes emerged from the data analysis: Worth the Risk as Long as You Do It Well; and, Mismatch Between Envisioned Uses and Current Reality. Participants noted that AI could have value if used for specific purposes, for example: supporting care for patients; reducing practitioner burden; analyzing existing evidence; managing patient populations; and, supporting operational efficiencies. Participants identified facilitators of AI being used for these purposes including: use of relevant case studies/success stories with realistic uses of AI highlighted; easy or low risk applications; and, end user involvement. However, barriers to the use of AI included: data quality; digital divide/equity; distrust of AI including security/privacy issues; for-profit motives; need for transparency about how AI works; and, fear about impact on practitioners regarding clinical judgement. Conclusion: AI will continue to become more prominent in primary health care. There is potential for positive impact, however there are many factors that need to be considered regarding the implementation of AI. The findings of this study can help to inform the development and deployment of AI tools in primary health care., Competing Interests: Authors report none., (2021 Annals of Family Medicine, Inc.)

CONTEXT: Artificial intelligence (AI) is increasingly being recognized as having potential importance to primary care (PC). However, there is a gap in our understanding about where to focus efforts related to AI for PC settings, especially given the current COVID-19 pandemic. OBJECTIVE: To identify current priority areas for AI and PC in Ontario, Canada. STUDY DESIGN: Multi-stakeholder engagement event with facilitated small and large group discussions. A nominal group technique process was used to identify and rank challenges in PC that AI may be able to support. Mentimeter software was used to allow real-time, anonymous and independent ranking from all participants. A final list of priority areas for AI and PC, with key considerations, was derived based on ranked items and small group discussion notes. SETTING: Ontario, Canada. POPULATION STUDIED: Digital health and PC stakeholders. OUTCOME MEASURES: N/A. RESULTS: The event included 8 providers, 8 patient advisors, 4 decision makers, 3 digital health stakeholders, and 12 researchers. Nine priority areas for AI and PC were identified and ranked, which can be grouped into those intended to support physician (preventative care and risk profiling, clinical decision support, routine task support), patient (self-management of conditions, increased mental health care capacity and support), or system-level initiatives (administrative staff support, management and synthesis of information sources); and foundational areas that would support work on other priorities (improved communication between PC and AI stakeholders, data sharing and interoperability between providers). Small group discussions identified barriers and facilitators related to the priorities, including data availability, quality, and consent; legal and device certification issues; trust between people and technology; equity and the digital divide; patient centredness and user-centred design; and the need for funding to support collaborative research and pilot testing. Although identified areas do not explicitly mention COVID-19, participants were encouraged to think about what would be feasible and meaningful to accomplish within a few years, including considerations of the COVID-19 pandemic and recovery phases. CONCLUSIONS: A one-day multi-stakeholder event identified priority areas for AI and PC in Ontario. These priorities can serve as guideposts to focus near-term efforts on the planning, development, and evaluation of AI for PC., Competing Interests: Authors report none., (2021 Annals of Family Medicine, Inc.)

Despite widespread advancements in and envisioned uses for artificial intelligence (AI), few examples of successfully implemented AI innovations exist in primary care (PC) settings., Objectives: To identify priority areas for AI and PC in Ontario, Canada., Methods: A collaborative consultation event engaged multiple stakeholders in a nominal group technique process to generate, discuss and rank ideas for how AI can support Ontario PC., Results: The consultation process produced nine ranked priorities: (1) preventative care and risk profiling, (2) patient self-management of condition(s), (3) management and synthesis of information, (4) improved communication between PC and AI stakeholders, (5) data sharing and interoperability, (6-tie) clinical decision support, (6-tie) administrative staff support, (8) practitioner clerical and routine task support and (9) increased mental healthcare capacity and support. Themes emerging from small group discussions about barriers, implementation issues and resources needed to support the priorities included: equity and the digital divide; system capacity and culture; data availability and quality; legal and ethical issues; user-centred design; patient-centredness; and proper evaluation of AI-driven tool implementation., Discussion: Findings provide guidance for future work on AI and PC. There are immediate opportunities to use existing resources to develop and test AI for priority areas at the patient, provider and system level. For larger scale, sustainable innovations, there is a need for longer-term projects that lay foundations around data and interdisciplinary work., Conclusion: Study findings can be used to inform future research and development of AI for PC, and to guide resource planning and allocation., Competing Interests: Competing interests: The INSPIRE-PHC program provided funds to Western University to support this research project, including staff contracts. Some authors receive salary support from Western University or the University of Toronto and used time from within their roles at those institutions to engage in the research project., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The scale-up of evidence-based innovations is required to reduce waste and inequities in health and social services (HSS). However, it often tends to be a top-down process initiated by policy makers, and the values of the intended beneficiaries are forgotten. Involving multiple stakeholders including patients and the public in the scaling-up process is thus essential but highly complex. We propose to identify relevant strategies for meaningfully and equitably involving patients and the public in the science and practice of scaling up in HSS., Methods: We will adapt our overall method from the RAND/UCLA Appropriateness Method. Following this, we will perform a two-prong study design (knowledge synthesis and Delphi study) grounded in an integrated knowledge translation approach. This approach involves extensive participation of a network of stakeholders interested in patient and public involvement (PPI) in scaling up and a multidisciplinary steering committee. We will conduct a systematic scoping review following the methodology recommended in the Joanna Briggs Institute Reviewers Manual. We will use the following eligibility criteria: (1) participants-any stakeholder involved in creating or testing a strategy for PPI; (2) intervention-any PPI strategy proposed for scaling-up initiatives; (3) comparator-no restriction; (4) outcomes: any process or outcome metrics related to PPI; and (5) setting-HSS. We will search electronic databases (e.g., Medline, Web of Science, Sociological Abstract) from inception onwards, hand search relevant websites, screen the reference lists of included records, and consult experts in the field. Two reviewers will independently select and extract eligible studies. We will summarize data quantitatively and qualitatively and report results using the PRISMA extension for Scoping Reviews (PRISMA-ScR) checklist. We will conduct an online Delphi survey to achieve consensus on the relevant strategies for PPI in scaling-up initiatives in HSS. Participants will include stakeholders from low-, middle-, and high-income countries. We anticipate that three rounds will allow an acceptable degree of agreement on research priorities., Discussion: Our findings will advance understanding of how to meaningfully and equitably involve patients and the public in scaling-up initiatives for sustainable HSS., Systematic Review Registration: We registered this protocol with the Open Science Framework on August 19, 2020 ( https://osf.io/zqpx7/ ).

Aim: To describe the proceedings and outcomes of a Locating Technology and Dementia Forum that brought together 109 representatives of researchers, product manufacturers, policy makers, Alzheimer Societies, clinicians, first responders, persons with dementia and care partners., Methods: Information gathered from this event was used to create strategic direction for advancing the development and use of locating technologies among persons with dementia., Results: Key recommendations from this forum include the need to: fund and conduct research pertaining to usability and effectiveness of technologies; increase awareness about the risk of missing person events; develop a guideline of strategies to manage critical wandering; and engage users in technology development and evaluation., Conclusion: Results are being used to guide research and to inform policies directed at the management of dementia-related wandering.