A blended learning curriculum for training peer researchers to conduct community-based participatory research
Bibliographic record
Abstract
Peer researchers (PRs) are research team members who share traits (e.g. gender, age, sexual orientation, diagnosis, income, housing situation, etc.) with study participants. Participatory methods and some fields (e.g. HIV/AIDS) expect PRs to be equitably involved in a project. Moreover, in Canada, there is a current impetus to include ‘the patient’ in health research. PRs often join a project without any formal research training, yet they are frequently tasked with suggesting appropriate language, recruiting participants, conducting interviews, administering surveys, analyzing data, and presenting findings. While there is literature on PR hiring, ethical considerations of PR engagement, and PR experiences, the methods of training PRs remain underreported. A blended learning curriculum (i.e. combination of webinars, didactic in-person presentation, filmed simulation, etc.), informed by the principles of action learning and the concept of reciprocity, has shown preliminary effectiveness in training PRs across two studies. This paper will present the curriculum, alongside exploratory evaluation results (n = 7), with details on how the curriculum changed from one study to the next and how reciprocity between academic and peer researchers led to stronger collaborations.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".