Participatory Action Research Among People With Serious Mental Illness: A Scoping Review
Bibliographic record
Abstract
Participatory action research (PAR) is a research approach that creates spaces for marginalized individuals and communities to be co-researchers to guide relevant social change. While working toward social transformation, all members of the PAR team often experience personal transformation. Engaging people with serious mental illness (PSMI) in PAR helps them to develop skills and build relationships with stakeholders in their communities. It supports positive changes that persist after the completion of the formal research project. With the increasing recognition of PAR's value in PSMI, it is helpful to consider the challenges and advantages of this approach to research with this population. This review aimed at determining how PAR has been conducted with PSMI and at summarizing strategies used to empower PSMI as co-researchers by engaging them in research. This scoping review followed five steps Arkesy and O'Malley (2005) outlined. We charted, collated, and summarized relevant information from 87 studies that met the inclusion criteria. We identified five strategies to empower PSMI through PAR. These are to build capacity, balance power distribution, create collaborative environments, promote peer support, and enhance their engagement as co-researchers. In conclusion, PAR is an efficient research approach to engage PSMI. Further, PSMI who engage in PAR may benefit from strategies for empowerment that meet their unique needs as co-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 | 0.041 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".