Long-term care (LTC) residents are a previously untested and highly vulnerable population at risk of elder abuse (EA) and its many negative health outcomes. The detection of elder abuse within the LTC context is urgent and time-sensitive. The overarching aim of this study is to evaluate the feasibility of implementing the Elder Abuse Suspicion Index – long-term care (EASI-ltc © ): the first comprehensive detection tool of its kind designed specifically to identify the abuse of cognitively-apt persons living in LTC. Preliminary tool validity will also be evaluated. This observational pilot study is taking place within seven LTC institutions in Montreal Canada. It begins with the administration of the EASI-ltc on a random sample of eligible LTC residents. Residents subsequently undergo a follow-up assessment led by a trained and experienced social worker, which is used as the ‘silver standard’ reference for validation. Residents are asked to reflect on their own experiences as participants in one on-one interviews, and key stakeholders (e.g., administrators, staff members, family, companions, and friends) are asked to provide retroactive feedback via an online survey. Survey respondents are also invited to participate in interviews to clarify their responses. Potential harmful consequences arising from EASI-ltc administration is being evaluated. Results pertaining to tool validity, feasibility, and acceptability obtained from data collected during the first 8 months will be presented. The EASI-ltc is the first published comprehensive tool to detect elder abuse in this vulnerable population. This ongoing study responds to an urgent need to provide tools to identify abuse and give a voice to LTC residents locally, nationally, and beyond.

Background:Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate management are needed to decrease mortality and morbidity. Artificial intelligence's (AI) potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents' mental health. Objective:This review aimed to identify AI interventions that have been tested, implemented, or both, for use in adolescents' mental health care. Methods:We used the Arksey and O'Malley framework, further refined by Levac et al, along with the Joanna Briggs Institute methodology, to guide this scoping review. We searched 5 electronic databases from the inception date through July 2024 (inclusive). Four independent reviewers screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a fifth reviewer was sought. We evaluated the risk of bias (ROB) for prognosis and diagnosis-related studies using the Prediction Model Risk of Bias Assessment Tool. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting. Results:Of the papers screened, 88 papers relevant to our eligibility criteria were identified. Among the included papers, AI was most commonly used for diagnosis (n=78), followed by monitoring and evaluation (n=19), treatment (n=10), and prognosis (n=6). As some studies addressed multiple applications, categories are not mutually exclusive. For diagnosis, studies primarily addressed suicidal behaviors (n=11) and autism spectrum disorder (n=7). Machine learning was the most frequently reported AI method across all application areas. The overall ROB for diagnostic and prognostic models was predominantly unclear (58%), while 20% of studies had a high ROB and 22% were assessed as low risk. Conclusions:In our review, we found that AI is being applied across various areas of adolescent mental health care, spanning diagnosis, treatment planning, symptom monitoring, and prognosis. Interestingly, most studies to date have concentrated heavily on diagnostic tools, leaving other important aspects of care relatively underexplored. This presents a key opportunity for future research to broaden the scope of AI applications beyond diagnosis. Moreover, future studies should emphasize the meaningful and active involvement of end users in the design, development, and validation of AI interventions, alongside improved transparency in reporting AI models, data handling, and analytical processes to build trust and support safe clinical implementation.

Background Intervention adaptation, the deliberate modification of the design or delivery of interventions to a new context, is more resource efficient than de novo development. However, adaptation must be approached methodically, as some modifications, such as those to the core components, may compromise the intervention’s initial efficacy. While adaptation frameworks have been published, none have been identified as more likely to result in successful adaptations. Further, frameworks lack the step-by-step details needed for operationalization. Therefore, the goal of this paper is to share our experience in addressing these methodological limitations in intervention adaptation. The objectives were to describe: 1) our development of a step-by-step, theoretically and empirically driven approach to intervention adaptation labelled the ConsoLidated AppRoach to Intervention adaptatiON (CLARION), 2) the application of CLARION in adapting a depression self-management intervention, 3) the facilitators and challenges encountered when using CLARION. Methods The development of CLARION was informed by the Medical Research Council guidance, the Method for Program Adaptation through Community Engagement (M-PACE), and a published scoping review identifying the key steps in existing adaptation frameworks. M-PACE was selected for its patient-oriented research principles, its application to a similar complex intervention, and for offering some of the specificity needed for execution. However, the scoping review indicated that M-PACE lacked three critical steps: selecting a candidate intervention, understanding its core components, and pre-testing the adapted intervention. These were added to form CLARION, which was structured in two stages: the first involves selecting an intervention, identifying core components, and deciding on modifications; the second stage solicits interest stakeholder feedback to assess the acceptability of the preliminary adapted intervention (pre-test). Results Once CLARION was developed, it was put into action to adapt a depression self-management intervention. CLARION demonstrated several strengths: 1) clearly articulating core components before deciding on modifications, 2) mobilizing a diverse steering committee of experts, including patient partners and developers of the original intervention, which balanced input and efficiency, and 3) establishing committee decision-making rules prior to adjudication (specific criteria and 75% supermajority). Key challenges included defining the types of modifications requiring committee input, determining the extent of the committee’s involvement, and prioritizing the presence of all committee members at meetings to avoid difficulties integrating incongruent feedback. Conclusions The development of CLARION contributes to best practices for intervention adaptation by identifying step-by-step guidance as well as facilitators and barriers to its application.

BackgroundAs health care moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of artificial intelligence (AI). However, family medicine training in AI has often been inconsistent or lacking. ObjectiveThe aim of the study is to develop a curriculum framework for family medicine postgraduate education on AI called “Artificial Intelligence Training in Postgraduate Family Medicine Education” (AIFM-ed). MethodsFirst, we conducted a comprehensive scoping review on existing AI education frameworks guided by the methodological framework developed by Arksey and O’Malley and Joanna Briggs Institute methodological framework for scoping reviews. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Next, 2 national expert panels were conducted. Panelists included family medicine educators and residents knowledgeable in AI from family medicine residency programs across Canada. Participants were purposively sampled, and panels were held via Zoom, recorded, and transcribed. Data were analyzed using content analysis. We followed the Standards for Reporting Qualitative Research for panels. ResultsAn integration of the scoping review results and 2 panel discussions of 14 participants led to the development of the AIFM-ed curriculum framework for AI training in postgraduate family medicine education with five key elements: (1) need and purpose of the curriculum, (2) learning objectives, (3) curriculum content, (4) organization of curriculum content, and (5) implementation aspects of the curriculum. ConclusionsUsing the results of this study, we developed the AIFM-ed curriculum framework for AI training in postgraduate family medicine education. This framework serves as a structured guide for integrating AI competencies into medical education, ensuring that future family physicians are equipped with the necessary skills to use AI effectively in their clinical practice. Future research should focus on the validation and implementation of the AIFM-ed framework within family medicine education. Institutions also are encouraged to consider adapting the AIFM-ed framework within their own programs, tailoring it to meet the specific needs of their trainees and health care environments.

Background The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, and caregivers. Socially assistive robots (SARs) offer promising supportive care to assist in the complex management associated with AD. Objectives To conduct a scoping review of published articles that proposed, discussed, developed or tested SAR for interacting with AD patients. Methods A scoping review framework suggested by Arksey and O'Malley was used from the date of inception until January 2022 in eight bibliographic databases. The inclusion criteria were all populations; all interventions using SAR for AD; any outcome related to AD patients or care providers or caregivers; all study types; and English language. Results After deduplication, 1251 articles were screened. Titles and abstracts screening resulted to 252 articles. Full-text review retained 125 articles, with 72 focusing on daily life support, 46 on cognitive therapy, and 7 on cognitive assessment. Conclusion This study identified current research and gaps in SARs for AD patients. However, there are still technical challenges. These include the necessity to engage patients, care providers and caregivers from both genders in the investigations about end-user needs, assess the impact of SAR type on HRI, providing more details about their methods for AI-powered SARs, and considering additional factors such as usability, safety, etc. that were not thoroughly investigated in this study. Investigation of these findings could resolve some of the current technological limitations, and they may help SARs reach nearly the full potential.

Background Given that mental health problems in adolescence may have lifelong impacts, the role of primary care physicians (PCPs) in identifying and managing these issues is important. Artificial Intelligence (AI) may offer solutions to the current challenges involved in mental health care. We therefore explored PCPs' challenges in addressing adolescents' mental health, along with their attitudes towards using AI to assist them in their tasks.Methods We used purposeful sampling to recruit PCPs for a virtual Focus Group (FG). The virtual FG lasted 75 minutes and was moderated by two facilitators. A life transcription was produced by an online meeting software. Transcribed data was cleaned, followed by a priori and inductive coding and thematic analysis.Results We reached out to 35 potential participants via email. Seven agreed to participate, and ultimately four took part in the FG. PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients' data, and relatively credible. They envisioned AI assisting with tasks such as diagnoses and establishing treatment plans. However, they feared that reliance on AI might result in a loss of clinical competency. PCPs wanted AI systems to be user-friendly, and they were willing to assist in achieving this goal if it was within their scope of practice and they were compensated for their contribution. They stressed a need for regulatory bodies to deal with medicolegal and ethical aspects of AI and clear guidelines to reduce or eliminate the potential of patient harm.Conclusion This study provides the groundwork for assessing PCPs' perceptions of AI systems' features and characteristics, potential applications, possible negative aspects, and requirements for using them. A future study of adolescents' perspectives on integrating AI into mental healthcare might contribute a fuller understanding of the potential of AI for this population.

Background Whole person care has been a foundation of the modern practice of family medicine, and therefore one might expect that such an approach by family practitioners would evolve and mature over time. Some of these clinicians, however, currently question their ability to provide such holistic care because of external factors they perceive as negative, and over which they have little or no control. Objectives This presentation will explore perceived inhibiting factors affecting family doctors ability to provide whole person care within the publicly-funded health care system of the Canadian province of Quebec. Method The presenter, an academic and clinician scientist with forty-four years of experience practicing family medicine, will present personal perspectives, along with those gathered informally from a broad cadre of colleagues working in different settings. Using the idiom of “elephant in the room” to identify problems or obstacles that may not be voiced, this talk will start with consideration of elephant anatomy as being comprised of thirteen distinct anatomical parts that contribute to a functional whole. An analogy will be developed in which thirteen distinct factors are presented as likely impeding or discouraging whole person care by family physicians. Conclusion Some agendas and policies of health care planners and administrators, either alone or collectively, and intentionally or unintentionally, may decrease opportunity or ability of family physicians to provide whole person care.

BACKGROUND:The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, and caregivers. Socially assistive robots (SARs) offer promising supportive care to assist in the complex management associated with AD. OBJECTIVES:To conduct a scoping review of published articles that proposed, discussed, developed or tested SAR for interacting with AD patients. METHODS:We performed a scoping review informed by the methodological framework of Arksey and O'Malley and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. At the identification stage, an information specialist performed a comprehensive search of 8 electronic databases from the date of inception until January 2022 in eight bibliographic databases. The inclusion criteria were all populations who recive or provide care for AD, all interventions using SAR for AD and our outcomes of inteerst were any outcome related to AD patients or care providers or caregivers. All study types published in the English language were included. RESULTS:After deduplication, 1251 articles were screened. Titles and abstracts screening resulted to 252 articles. Full-text review retained 125 included articles, with 72 focusing on daily life support, 46 on cognitive therapy, and 7 on cognitive assessment. CONCLUSION:We conducted a comprehensive scoping review emphasizing on the interaction of SAR with AD patients, with a specific focus on daily life support, cognitive assessment, and cognitive therapy. We discussed our findings' pertinence relative to specific populations, interventions, and outcomes of human-SAR interaction on users and identified current knowledge gaps in SARs for AD patients.

BackgroundThe COVID-19 pandemic has required family physicians to rapidly address increasing mental health problems with limited resources. Vulnerable home-based seniors with chronic physical conditions and commonly undermanaged symptoms of anxiety and depression were recruited in this pilot study to compare two brief self-care intervention strategies for the management of symptoms of depression and/or anxiety.MethodsWe conducted a pilot RCT to compare two tele-health strategies to address mental health symptoms either with 1) validated CBT self-care tools plus up to three telephone calls from a trained lay coach vs. 2) the CBT self-guided tools alone. The interventions were abbreviated from those previously trialed by our team, to enable their completion in 2 months. Objectives were to assess the feasibility of delivering the interventions during a pandemic (recruitment and retention); and assess the comparative acceptability of the interventions across the two groups (satisfaction and tool use); and estimate preliminary comparative effectiveness of the interventions on severity of depression and anxiety symptoms. Because we were interested in whether the interventions were acceptable to a wide range of older adults, no mental health screening for eligibility was performed.Results90 eligible patients were randomized. 93% of study completers consulted the self-care tools and 84% of those in the coached arm received at least some coaching support. Satisfaction scores were high among participants in both groups. No difference in depression and anxiety outcomes between the coached and non-coached participants was observed, but coaching was found to have a significant effect on participants' use and perceived helpfulness of the tools.ConclusionBoth interventions were feasible and acceptable to patients. Trained lay coaching increased patients' engagement with the tools. Self-care tools offer a low cost and acceptable remote activity that can be targeted to those with immediate needs. While effectiveness results were inconclusive, this may be due to the lack of eligibility screening for mental health symptoms, abbreviated toolkit, and fewer coaching sessions than those used in our previous effective interventions.Trial registrationClinicalTrials.gov Identifier: NCT0460937.

Objective: To synthesize results of six controlled trials of self-care interventions for depression and/or anxiety, focusing on five trials in which lay guidance was compared to self-directed use of the same self-care tools. Methods: The trials were conducted in Canada in different target populations. Self-care tools were adapted to each population. Guidance was provided in 3-15 calls over a period of 6-26 weeks. Depression and/or anxiety were assessed at follow-up (6-26 weeks). Pooled analyses used a meta-analytic approach. Engagement with the self-care tools was compared using the standardized difference or Cohen's d effect size. Results: In studies with homogeneous outcomes (three for depression, four for anxiety), the pooled effect sizes of guidance vs. self-directed use of the self-care tools were 0.36 (95% CI 0.10, 0.62, N = 235) for depression and 0.21 (95% CI-0.03, 0.44, N = 285) for anxiety. Guidance consistently led to greater engagement with the tools. Conclusions: The intervention model is a potentially sustainable and accessible alternative to professionally guided self-care for people with mild-moderate depression. Factors which may have limited implementation success include: co-interventions, reduced number of guide calls (3 vs 6 or more), and delivery to dyads (patient -caregiver).

Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate management are needed to decrease mortality and morbidity. Artificial Intelligence (AI) 's potential contributions, although significant in medicine, have not been adequately studied in this context. Therefore, this review aimed to identify AI interventions that have been tested and/or implemented for use in adolescents' mental healthcare. We used Levac et al. and the Joanna Briggs Institute scoping review framework for conducting this review. We followed PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the study. We searched five electronic databases from inception date until February 2020. Two independent reviewers screened the articles and extracted the data. A third reviewer resolved disagreements. We evaluated the risk of bias for prognosis and diagnosis-related studies using Prediction model Risk Of Bias Assessment Tool (PROBAST). We retrieved 1044 records, and 30 papers were included after the two-level screening. In the included papers, AI was used for diagnostic (n=23), monitoring and evaluation (n=8), treatment (n=5), and prognosis (n=2). For diagnosis purposes, the papers focused on: autism spectrum disorder (n=3) and unspecified outcomes of psychological stress/pressure level in adolescents (n=3), followed by substance use disorder (n=2) and dysfunctional behavior in adolescents (n=2). Machine learning methods were the most frequently reported AI methods. The "overall" risk of bias for diagnosis/prognosis models was unclear (84%) Overall, within our included papiers, AI has been utilized to support various aspects of adolescent mental healthcare, including diagnosis, treatment, monitoring, and prognosis. However, there is a notable emphasis on diagnosis in the available literature, with limited exploration of AI applications in other areas. This research gap should be addressed in future studies. Moreover, future studies should address work on meaningful and active involvement of end-users in designing, developing, and validating AI interventions, as well as better reporting of AI models, data collection, and analysis process.

The objective of this study was to evaluate the implementation and outcomes of a quality improvement intervention for older adults discharged from hospital to home, that used a patient-centred discharge education tool called the Patient-Centered Discharge Plan (PCAP). We conducted a pre-post evaluation of PCAP implementation among patients 65 years and older and discharged home from an acute medical or geriatric admission at two general hospitals. Two patient cohorts, PRE and POST, were analysed using administrative data (n = 3,309) and post-discharge structured interviews in a subset of patients (n = 326). Outcomes were 90-day readmissions and return emergency department (ED) visits, and transition experiences (10-item scale). The PCAP was provided to 20 per cent of 1,683 patients. Transition experience scores increased from PRE to POST at both hospitals (adjusted beta 1.3; 95% CI: 0.8, 1.7), and return ED visits declined in one of the two hospitals (adjusted decline 1.3%; 95% CI: -3.7, 6.2). In conclusion, dedicated resources are needed to support future PCAP implementation.

Objectives: Identify the key effective components of a depression self-care intervention. Methods: Secondary analysis of data from 3 studies that demonstrated effectiveness of a similar depression self-care intervention (n = 275): 2 studies among patients with chronic physical conditions and 1 among cancer survivors. The studies used similar tools, and telephone-based lay coaching. Depression remission and reduction at 6 months were assessed with either PHQ-9 (chronic condition cohorts) or CES-D (cancer survivor cohort). Multiple logistic regression was used to analyze data when the interaction p-value with cohort was < 0.10. Results: The 3 coached cohorts achieved better depression outcomes than usual care. The combination of coaching and joint use of 2 tools based on cognitive-behavioral therapy (CBT) was associated with depression remission and reduction among chronic condition cohorts but not among cancer survivors. Neither the number nor the length of coach calls were associated with outcomes in pooled data. Conclusions: Trained lay coaching and use of CBT-based self-care tools were associated with improved depression outcomes in patients with chronic conditions but not among cancer survivors. Practice implications: Trained lay coaching and CBT tools are key components of depression self-care interventions. Further research is needed on the effective components in cancer survivors.

Psychometrically sound measures of chronic disease self-management tasks are needed to improve identification of patient needs and to tailor self-management programs. This study aimed to develop and conduct a preliminary psychometric analysis of the CanSMART questionnaire among a diverse, multimorbid Canadian population. The data were drawn from a cross-sectional online survey to examine self-management needs and support preferences. Participants were 306 Canadian adults with one or more physical and/or emotional chronic conditions. The questionnaire on frequency of self-management tasks was developed with substantial patient partner input. We conducted Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) of the 11 self-management tasks comprising the scale in two randomly selected subsamples, followed by Rasch analysis. Associations between patient characteristics and the self-management task subscales and individual items were explored. The factor analyses identified two self-management task subscales that were labelled Coping tasks (6 items) and Physical tasks (3 items), with Cronbach’s alpha of 0.70 and 0.67, respectively. Rasch analysis suggested that participants had difficulty discriminating between response options “mostly” and “always”. In analyses of independent associations with patient characteristics, both Coping and Physical tasks were associated with reporting more than one chronic disease and employment disability. The Coping tasks subscale was associated with female sex. Two items, on medication use and monitoring biological parameters, did not load on either scale. Both were associated with specific diagnoses. In this preliminary analysis, two self-management tasks subscales exhibit good psychometric properties. Two items that did not load on either scale may represent additional dimensions of self-management. This work provides the basis for further scale development and use in research and clinical practice.

The COVID-19 pandemic propelled many physicians and their patients into an unfamiliar world of virtual care. This presentation is based on the perceptions of a family physician/ teacher/ researcher with 43 years of interest in, and promotion of, a strong doctor-patient relationship. It will describe a protocol that governed how tele-medicine and video-conferencing took place over nearly 18 months in his practice. It will then describe observed positive and negative impacts for the patients, their family members, the physician, and members of the family medicine health care team. Interpretation will be made about what such observations mean for the doctor-patient relationship.

PURPOSE Depression in post-treatment cancer survivors is common and can impair quality of life. CanDirect is a novel, telephone-delivered depression self-care intervention for cancer survivors. We conducted a randomized controlled superiority trial to compare CanDirect with usual care (UC) in this population. METHODS Participants completing cancer treatment within the past 10 years who had mild-moderate depressive symptoms with or without major depression were recruited from clinical and community settings in Quebec and Ontario. Permuted block random assignment allocated participants to CanDirect plus UC or to UC alone. Assessments of depression severity (Center for Epidemiological Studies-Depression scale [CES-D]; primary outcome) and secondary outcomes health-related quality of life (Short Form Survey-12 mental and physical component summaries), anxiety symptoms (Hospital Anxiety and Depression Scale), activation (Patient Activation Measure), depression diagnosis (Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV), and health services (self-report) were conducted at baseline, as well as 3 and 6 months (primary time point). Analyses of outcomes were adjusted for covariates using linear regression and missing data by inverse probability weighting. RESULTS Participants recruited between September 2016 and October 2018 were randomly assigned to CanDirect (n = 121) or UC (n = 124). Among 245 participants randomly assigned, 218 (89.0%) completed the primary outcome at 6 months. CanDirect participants reported less severe depressive symptoms on the CES-D than UC participants at 6 months, adjusted effect size (ES) 0.61 (95% CI, 0.33 to 0.88). CanDirect participants also had significantly greater quality of life, lower anxiety, more activation, and lower rates of depression diagnoses, compared with UC. Exploratory analysis suggested that sex was a modifier of the primary outcome (interaction term P value = .03); the intervention was less effective in men (ES, 0.12; 95% CI, -0.45 to 0.69). CONCLUSION The findings suggest that CanDirect is an effective method of managing mild-moderate depression symptoms in cancer survivors.

Purpose The PACIC assesses key components of the Chronic Care Model. The purpose of this study is to examine the dimensionality and psychometric properties of the PACIC. Methods A convenience sample of 221 adults in Canada who self-identified as living with one or more physical and/or mental chronic diseases was invited to participate via an online survey link. Rasch analysis was performed, including item and person misfit, reliability, response format, targeting, unidimensionality of subscales, and differential item functioning (DIF). Also, Confirmatory Factor Analysis (CFA) was conducted and model fit of alternative factor structures proposed for the PACIC in the literature and those suggested by the Rasch analysis were explored. Results The patient activation, delivery system, and problem-solving subscales fit the Rasch model expectations; no modifications were required. The goal setting item 10 had a disordered threshold and was recoded. Four of the five follow-up subscale items had a disordered threshold and were recoded. All subscales were unidimensional and no local dependency was detected. DIF was only detected for some items in the follow-up subscale. The CFA revealed that none of the published factor structures fit the data; the fit statistics were appropriate when item 10 was removed and the follow-up subscale was removed. Conclusions Improving chronic disease care relies upon having validated measures to evaluate the extent to which care goals are met. With some modifications, four of the five PACIC subscales were found to be psychometrically robust.

Objectives: Among Canadian adults with chronic disease: 1) to identify groups that differ in self-management task frequency and self-efficacy; 2) to compare group characteristics and preferences for self-management support. Methods: Using data from an online survey, cluster analysis was used to identify groups that differed in self-management task frequency and self-efficacy. Multivariable regression was used to explore relationships with patient characteristics and preferences. Results: Cluster analysis (n = 247) revealed three groups:Vulnerable Self-Managers (n = 55), with the highest task frequency and lowest self-efficacy; Confident Self-Managers (n = 73), with the lowest task frequency and highest self-efficacy; and Moderate Needs Self-Managers (n = 119), with intermediate task frequency and self-efficacy. Vulnerable Self-Managers, when compared with the Confident group, were more often: on illness-related employment disability or unemployed; less well educated; diagnosed with emotional problems or hypertension, and had greater multimorbidity. They participated less often in self-management programs, and differed in support preferences. Conclusions: Knowing the characteristics of vulnerable self-managers can help in targeting those in greater need for self-management support that matches their preferences. Practice Implications: Different approaches are needed to support self-management in the vulnerable population. (C) 2019 Published by Elsevier B.V.

A framework of social inclusion can promote equity and aid in preventing and addressing the abuse of older adults. Our objective was to build a social inclusion framework for a comprehensive hospital-based elder abuse intervention being developed. Potential components of such a framework, namely, health determinants and guiding principles, were extracted from a systematic scoping review of existing responses (e.g., interventions, protocols) to elder abuse and collated. These were subsequently rated for their importance to the elder abuse intervention by a panel of violence experts and further evaluated by a panel of elder abuse experts. The final social inclusion framework comprised 12 health determinants each representing factors underpinning susceptibility for abuse in aging populations: history of trauma/abuse, communication needs, disability, health status, mental capacity, social support, culture, language, sexuality, religion, gender identity, and socioeconomic status. The framework also comprised 19 guiding principles each encompassing considerations for equitable engagement with older adults (e.g., All older adults have the right to self-determination, All older adults have the right to be safe, All older adults are assumed competent unless determined otherwise). Integrating this social inclusion framework into the design and delivery of an elder abuse intervention could empower older adults, while at the same time ensuring that practices and policies are tailored to meet their unique and varying needs.