Building recovery capital through peer harm reduction work
Bibliographic record
Abstract
Purpose – Peer harm reduction programmes engage service users in service delivery and may help peers to develop employment skills, better health, greater stability, and new goals. Thus far, peer work has not been discussed as an intervention to promote recovery. The purpose of this paper is to provide findings related to two research questions: first,do low-threshold employment programmes have the potential to contribute to positive recovery capital, and if so, how? Second, how are such programmes designed and what challenges do they face in supporting the recovery process? Design/methodology/approach – Using a community-based research approach, data were collected at a Toronto, Canada community health centre using in-depth interviews with peer workers (n=5), staff (n=5), and programme clients (n=4) and two focus groups with peer workers (n=12). A thematic analysis was undertaken to describe the programme model and to explore the mechanisms by which participation contributes to the development of recovery capital. Findings – The design of the Regent Park Community Health Centre peer work model demonstrates how opportunities for participation in community activities may spark cumulative growth in positive recovery capital within the community of PUDs. However, the recovery contagion of peer work may lose momentum with insufficient opportunities for new and experienced peer workers. Originality/value – Using the concept of recovery capital, the authors demonstrate how low-threshold employment interventions have the potential to contribute to the development of positive recovery capital.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".