A blended learning curriculum for training peer researchers to conduct community-based participatory research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.190 | 0.369 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it