Supporting people leaving prisons during COVID-19: perspectives from peer health mentors
Bibliographic record
Abstract
PURPOSE: Currently, people leaving prisons face concurrent risks from the COVID-19 pandemic and the overdose public health emergency. The closure or reduction of community services people rely on after release such as treatment centres and shelters has exacerbated the risks of poor health outcomes and harms. This paper aims to learn from peer health mentors (PHM) about changes to their work during overlapping health emergencies, as well as barriers and opportunities to support people leaving prison in this context. DESIGN/METHODOLOGY/APPROACH: The Unlocking the Gates (UTG) Peer Health Mentoring Program supports people leaving prison in British Columbia during the first three days after release. The authors conducted two focus groups with PHM over video conference in May 2020. Focus groups were recorded and transcribed, and themes were iteratively developed using narrative thematic analysis. FINDINGS: The findings highlighted the importance of peer health mentorship for people leaving prisons. PHM discussed increased opportunities for collaboration, ways the pandemic has changed how they are able to provide support, and how PHM are able to remain responsive and flexible to meet client needs. Additionally, PHM illuminated ways that COVID-19 has exacerbated existing barriers and identified specific actions needed to support client health, including increased housing and recovery beds, and tools for social and emotional well-being. ORIGINALITY/VALUE: This study contributes to our understanding of peer health mentorship during the COVID-19 pandemic from the perspective of mentors. PHM expertise can support release planning, improved health and well-being of people leaving prison and facilitate policy-supported pandemic responses.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".