Co-designing action-oriented mental health conversations: the case for integration in home and community care.
Bibliographic record
Abstract
Background: The COVID-19 pandemic has reinforced concerns for the mental health and wellness of older adults worldwide. Older adults experiencing poor mental health may face both mental health stigma and ageism, which act as barriers to talking about mental health and seeking needed supports, care, and treatment. In Canada and elsewhere, there is a lack of mental health parity. The healthcare system’s focus on the physical healthcare needs of older adults creates missed opportunities for intentional conversations about mental health between providers and older patients during routine interactions. Aims: This project responds to aging and mental health research priorities identified by aging Canadians during the pandemic through surveys (n=1,000) and workshops (n= 52 participants). Top priorities included the need for research and action supporting: 1) skill-building in non-mental health specialists; and 2) the application of user-friendly tools to identify signs of positive and poor mental health. The aim of this multi-year research study is to co-design and test an evidence-based approach to starting mental health conversations between home and community care providers, older adults and/or their family caregivers. Methods: This study applies a participatory mixed methods design across three phases, guided by a working group of experts-by-lived-experience (n=30). Phase 1 involved a modified ADAPTE process including online workshops (n= 57 participants) and surveys (n=~1000) of older adults, caregivers, and health/social care providers across Canada. The workshops explored the use of an evidence-based visual model, called the Mental Health Continuum, to guide mental health conversations in home and community care. Phase 2 will involve co-design workshops with community health/social care providers in six communities (n= 3 rural; n=3 urban) across three Canadian provinces. Phase 3 will involve pilot and feasibility testing of the co-designed conversations in routine care. This abstract focuses on the findings from Phase 1. Results: Workshop participants agreed that a visual model depicting mental health as a complex, multi-component construct ranging in state on a spectrum, was a helpful starting point for de-stigmatizing mental health between older adults, caregivers, and health/social care providers. However, participants felt the Mental Health Continuum needed to be adapted for use in promoting conversations in the context of home and community care. Suggested adaptations include the use of generic and action-oriented language across the model, more inclusive use of colours, capturing the element of change-over-time, removing clinical jargon from category names, and revising the signs and signals to be more aging context-relevant. Public consultation survey results are expected by the date of the conference. Learnings: Engaging experts-by-lived-experience in all study phases is crucial for building on existing evidence in new research through a realist lens and ensuring widespread health issues are met with context-specific and relevant solutions. Workshops reinforced research priorities identified during the pandemic and confirmed the study’s potential to address challenges with integration of health and social care for older adults through de-stigmatizing mental health conversations. Next steps: The adapted Mental Health Continuum model will guide the co-design and pilot-testing work in Phases 2 and 3 of the research study.
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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 | 0.134 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.031 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.009 | 0.040 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".