Participatory action research and photovoice in a psychiatric nursing/clubhouse collaboration exploring recovery narrative
Bibliographic record
Abstract
Accessible summary Personal stories about recovery in mental health are important sources of knowledge. Research methods are needed for exploring personal stories of recovery which honour and empower the authors of recovery stories. The Clubhouse of Winnipeg and an assistant professor in psychiatric nursing piloted a research project using photography in order to explore, document and share Clubhouse Member stories of recovery. Abstract The Clubhouse of Winnipeg (a community psychosocial rehabilitation centre) collaborated with a psychiatric nursing assistant professor on a participatory action research (PAR) project exploring the concept of recovery using a using a research method called photovoice. The collaborative project – Our Photos Our Voices – demonstrates how PAR and photovoice are well suited for collaborative research in mental health which honours principles underlying consumer empowerment and recovery. The foundation of empowerment is the power to act on one's behalf; PAR and photovoice support the full participation of concerned individuals in all aspects of research with the ultimate goal of action to solve problems or to meet goals identified by those individuals. Empowerment is also the ability to lay claim to one's own truth. At the core of the recovery model is the principle that recovery is defined by the individual and based on individual determinations of meaningful goals and a meaningful life. The Our Photos Our Voices project uses PAR and photovoice to effectively access, explore, document and share personal, local knowledge about recovery grounded in the personal experience of the Clubhouse researchers.
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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 | 0.042 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".