Engaging with peers to integrate community care: Knowledge synthesis and conceptual map
Bibliographic record
Abstract
CONTEXT: Engaging with peers is gaining increasing interest from healthcare systems in numerous countries. Peers are people who offer support by drawing on lived experiences of significant challenges or 'insider' knowledge of communities. Growing evidence suggests that peers can serve as a bridge between underserved communities and care providers across sectors, through their ability to build trust and relationships. Peer support is thus seen as an innovative way to address core issues of formal healthcare, particularly fragmentation of care and health inequalities. The wide body of approaches, goals and models of peer support speaks volumes of such interest. Navigating the various labels used to name peers, however, can be daunting. Similar terms often hide critical differences. OBJECTIVES/BACKGROUND: This article seeks to disentangle the conceptual multiplicity of peer support, presenting a conceptual map based on a 3-year knowledge synthesis project involving peers and programme stakeholders in Canada, and international scientific and grey literature. SYNTHESIS/MAIN RESULTS: The map introduces six key questions to navigate and situate peer support approaches according to peers' roles, pathways and settings of practice, regardless of the terms used to label them. As a tool, it offers a broad overview of the different ways peers contribute to integrating health and community care. DISCUSSION: We conclude by discussing the map's potential and limitations to establish a common language and bridge models, in support of knowledge exchange among practitioners, policymakers and researchers. PATIENT OR PUBLIC CONTRIBUTION: Our team includes one experienced peer support worker. She contributed to the design of the conceptual map and the production of the manuscript. More than 10 peers working across Canada were also involved during research meetings to validate and refine the conceptual map.
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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 | 0.070 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".