Peer researchers in post-professional healthcare: A glimpse at motivations and partial objectivity as opportunities for action researchers
Bibliographic record
Abstract
Peer researchers are members of a population under study who have a decision-making role or staff position on a research team. Peer researchers are increasingly required for funding proposals to succeed in Canadian HIV/AIDS research, and are strongly recommended for community-based participatory research in other fields. There is a need to better understand peer researchers’ motivations and their impact, both positive and negative, on studies they take part in. The emerging theory of post-professionalism informed a bounded system case study approach, whereby four peer researchers from an HIV, social work, and brain health study were conveniently sampled, then interviewed concerning their experiences and insider-outsider positioning. Personal interest and community leadership were key motivations behind their involvement; language barriers and managing multiple roles were key challenges. Participants identified a risk inherent in the performative interval, considering whether their contributions were a projection of self rather than a representation of participant contributions. Tension between social location and the insider positioning expected of peer researchers requires that academic researchers recognize the personal and social investments that peers make to a study. This paper presents considerations for how healthcare researchers can better engage as peers with peer 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 | 0.097 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.052 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.008 |
| 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".