From Disruption to Reconstruction: Implementing Peer Support in Homelessness During Times of Crisis for Health and Social Care Services
Bibliographic record
Abstract
Introduction: Peer support workers—people with a significant lived and living experience of a social or health condition—use their experiential knowledge and obtain training to help and care for others. They are integrated in different clinical settings, including those for people experiencing homelessness. Most research on peer support implementation in homelessness has not considered the timing of the implementation, particularly in periods of crisis. Description: During the COVID-19 pandemic crisis, a participatory research project examined the integration of a peer support worker in a primary and community care clinic that serves people experiencing homelessness in Montreal (Canada). This article presents a narrative case study analysis of the specific data on implementation derived from this project. Results: Three main learning points are of interest regarding implementation: 1) crises can precipitate challenges but also particular opportunities for the implementation of peer support initiatives in homelessness; 2) even during a crisis, certain key steps cannot be skipped when the goal is a successful implementation; and 3) research can be an external asset for clinical teams as they struggle to deliver care during periods of crisis. Conclusion: Peer support initiatives in homelessness can be implemented in the Canadian context during periods of crisis—for example, the COVID-19 pandemic—for health and social care services. Moreover, the concept of crisis itself can be reexamined by clinical and research teams worldwide as potentially enabling the implementation of novel initiatives.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 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".