Peer engagement in harm reduction strategies and services: a critical case study and evaluation framework from British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Engaging people with drug use experience, or 'peers,' in decision-making helps to ensure harm reduction services reflect current need. There is little published on the implementation, evaluation, and effectiveness of meaningful peer engagement. This paper aims to describe and evaluate peer engagement in British Columbia from 2010-2014. METHODS: A process evaluation framework specific to peer engagement was developed and used to assess progress made, lessons learned, and future opportunities under four domains: supportive environment, equitable participation, capacity building and empowerment, and improved programming and policy. The evaluation was conducted by reviewing primary and secondary qualitative data including focus groups, formal documents, and meeting minutes. RESULTS: Peer engagement was an iterative process that increased and improved over time as a consequence of reflexive learning. Practical ways to develop trust, redress power imbalances, and improve relationships were crosscutting themes. Lack of support, coordination, and building on existing capacity were factors that could undermine peer engagement. Peers involved across the province reviewed and provided feedback on these results. CONCLUSION: Recommendations from this evaluation can be applied to other peer engagement initiatives in decision-making settings to improve relationships between peers and professionals and to ensure programs and policies are relevant and equitable.
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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 | 0.071 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".