Examining the Feasibility, Acceptability, and Effectiveness of Remote Training on Community-Based Participatory Research: Single-Arm Pre-Post Pilot Study
Bibliographic record
Abstract
BACKGROUND: Over the past decade, a growing body of scientific evidence has demonstrated that community engagement in research leads to more relevant research, enhances the uptake of research findings, and improves clinical outcomes. Despite the increasing need for the integration of community engagement methodologies into the scientific inquiry, doctoral and master's level competencies in the field of psychiatry often lack dedicated training or coursework on community engagement methodologies. OBJECTIVE: A total of 13 service users, peer support specialists, caregivers of people with mental health challenges, and scientists (with specialties ranging from basic science to implementation science) aged 18 and older participated in remote training on community-based participatory research. Data were collected at baseline, 2 days, and 3 months. METHODS: A total of 13 service users, peer support specialists, caregivers of people with mental health challenges, and scientists (with specialties ranging from basic science to implementation science) aged 18 and older participated in remote training on community-based participatory research. Data were collected at baseline, 2 days, and 3 months. RESULTS: The pilot study demonstrated that a 3-month remote training on community-based participatory research ("Partnership Academy") was deemed feasible and acceptable by service users, peer support specialists, caregivers of people with mental health challenges, and scientists. Improvements were found in research engagement and the quality of partnership. A marked increase in distrust in the medical system was also found. Groups submitted 4 grant applications and published 1 peer-reviewed journal at a 3-month follow-up. CONCLUSIONS: This pre- and postpilot study demonstrated it is possible to train groups of service users, peer support specialists, caregivers of people with mental health challenges, and scientists in community-based participatory research. These findings provide preliminary evidence that a 3-month remote training on community-based participatory research ("Partnership Academy") is feasible, acceptable, and potentially associated with improvements in research engagement as well as the quality of partnership and output, such as manuscripts and grant applications.
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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 | 0.067 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".