Pause, re-think, go virtual … pandemic adaptations from 20 diverse mental health promotion intervention projects across Canada
Bibliographic record
Abstract
The Government of Canada's Mental Health Promotion Innovation Fund (MHP-IF) is a platform for learning across diverse projects, facilitated by a Knowledge Development and Exchange Hub. MHP-IF projects were getting underway before the COVID-19 pandemic escalated in 2020 and dramatically shifted their circumstances and activities. Using storytelling methods, this study explored 20 project experiences during the first year of the pandemic, including how and why assumptions, plans, and activities were adapted; early signals about what was working well or not; and how adaptations influenced equity, access, and cultural safety. Project teams generally navigated through four stages: pausing, re-thinking, adapting, and settling into adjustments. Within and across these stages, projects addressed similar processes, including meeting fundamental needs of participants and project teams, managing unanticipated benefits, and engaging with online formats. All projects experienced the pandemic's influence of amplifying both inequities and public and political attention on mental health. This study provides experiential evidence from diverse settings and populations in Canada about pandemic adaptations. The multi-project model and storytelling methods can usefully contribute to additional research, including ways to address inequities and promote cultural safety.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".