Peer and lay health work for people experiencing homelessness: A scoping review
Bibliographic record
Abstract
Homelessness poses complex health obstacles for individuals and communities. Peer and lay health worker programs aim to increase access to health care and improve health outcomes for PEH by building trust and empowering community-based workers. The scope and breadth of peer and lay health worker programs among PEH has not been synthesized. The primary objective of this scoping review is to understand the context (setting, community, condition or disease) encompassing peer and lay health worker programs within the homelessness sector. The secondary objective is to examine the factors that either facilitate or hinder the effectiveness of peer and lay health worker programs when applied to people experiencing homelessness (PEH). We searched CINHAL, Cochrane, Web of Science Core Collection, PsycINFO, Google Scholar and MEDLINE. We conducted independent and duplicate screening of titles and abstracts, and extracted information from eligible studies including study and intervention characteristics, peer personnel characteristics, outcome measures, and the inhibitors and enablers of effective programs. We discuss how peer and lay health work programs have successfully been implemented in various contexts including substance use, chronic disease management, harm reduction, and mental health among people experiencing homelessness. These programs reported four themes of enablers (shared experiences, trust and rapport, strong knowledge base, and flexibility of role) and five themes of barriers and inhibitors (lack of support and clear scope of role, poor attendance, precarious work and high turnover, safety, and mental well-being and relational boundaries). Organizations seeking to implement these interventions should anticipate and plan around the enablers and barriers to promote program success. Further investigation is needed to understand how peer and lay health work programs are implemented, the mechanisms and processes that drive effective peer and lay health work among PEH, and to establish best practices for these programs.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".