Integrating mental health conversations into home and community-based healthcare practice: making it ‘real’ through co-design with care providers across Canada
Bibliographic record
Abstract
Background: The Canadian healthcare system tends to focus on older adults’ physical needs, which leads to missed opportunities for integrated mental health support, care and treatment. Health and social care providers who work in community settings develop trusting therapeutic relationships with their clients, often in home environments—providing many insights into personal circumstances. These providers are well-positioned to talk about mental health with their clients, but these conversations are often avoided due to lack of evidence-based resources and training to support skill-building, confidence, and relevant referral knowledge. Aims: The overall aim of this study is to co-design and test an evidence-based approach to mental health conversations between providers, older adults, and family caregivers at the point-of-care in home and community settings across Canada. The objective for Phase 1 (presented at ICIC 2023) was to identify and adapt an evidence-based model describing mental health along a continuum. The objective for Phase 2 (focus for ICIC 2024) was to co-design point-of-care conversations rooted in the model from Phase 1. In Phase 3, the objective is to integrate the conversations into existing community care practices and test for feasibility. Methods: Our pan-Canadian research team including a working group of experts-by-experience (n=30) is conducting a 3-phase participatory, mixed-methods study over three years. Phase 1 involved four online workshops (n=59) and surveys (n=1069) with aging Canadians to adapt an existing Mental Health Continuum model. Phase 2 involved 7 co-design workshops with home and community care providers (n=84) in rural and urban communities across Ontario, British Columbia and Nova Scotia. Through interactive ‘gamestorming’ activities, participants co-created resources, tools, education and training needed to facilitate mental health conversations at the point-of-care. Workshop artefacts and transcripts were analyzed using framework analysis. Phase 3 involves pilot and feasibility testing of the co-designed conversations from phase 2. Results: An adapted model called the Mental Health Continuum for Aging Canadians (MHCAC) resulted from Phase 1. Phase 2 results include: 1) A conversation map to guide decisions to support tailoring of mental health conversations to an older adults’ unique circumstances (e.g., family caregiver presence; length of time on service; involvement of other providers); 2) A MHCAC toolkit including design blueprints for physical (e.g., magnets, pamphlets), digital (e.g., videos, podcast) and allegorical (e.g., living plants representing client well-being) formats; and 3) An implementation framework identifying foundational elements consistent across workshops (e.g., in-service training on MHCAC for providers) and variations by geography (e.g., paper-based preferences for rural sites, climate concerns for coastal provinces). Phase 3 findings are forthcoming and will be a focus for ICIC 2025. Learnings: Engaging experts-by-experience in co-designing applied care solutions is essential to producing knowledge that fits the real-world context. A multidimensional strategy rooted in consistent evidence, but with room for flexibility in approach, is necessary to enable mental health conversations at the point-of-care that will meet and respond to the diverse needs and circumstances of Canada’s aging population. Next Steps: Phase 3 is underway and will be ongoing through 2024 with 15 collaborating community organizations across Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".