We-Care-Well: exploring the personal recovery of mental health caregivers through Participatory Action Research
Bibliographic record
Abstract
Family caregivers play a critical role in supporting the recovery journeys of their loved ones, yet the recovery journeys of family caregivers have not been well-explored. Using a Participatory Action Research approach, we explore the personal recovery journeys of family caregivers for individuals with mental illness. This case study involved piloting and exploring the impact of a novel online workshop series offered to mental health caregivers at Ontario Shores Center for Mental Health Sciences. Recovery courses and workshops conventionally engage patients living with mental health conditions. In the current case, the recovery model is adapted to the needs and experiences of their family caregivers, resulting in a pilot workshop series called "We Care Well". Through participant-led discussions, interactive and take-home activities, and experiential learning, caregivers co-created workshop content and engaged in peer-learning on seven personal recovery-oriented topics. This included: self-care, resilience-building, non-violent communication, storytelling, and mental health advocacy. Throughout the sessions, participants implemented their learnings into their caregiving roles, and shared their experiences with the group to progress through their own recovery journeys. The We Care Well series was found to be an effective intervention to adapt and apply the personal recovery framework to mental health caregivers. PAR, and co-design are viable approaches to engage caregivers in mental health research, and can facilitate knowledge exchange, as well as relationship building with peers and program facilitators.
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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 | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".