Implementation of peer support in mental health services: A systematic review of the literature.
Bibliographic record
Abstract
Peer support within mental health services has a growing evidence base and aligns with current policies of recovery-oriented care. Despite these advantages, widespread implementation of peer support remains limited, likely due to various methodological and implementation issues. Researchers have noted the importance of utilizing an implementation framework to understand best practices for implementation. Therefore, the purpose of the current study was to synthesize the existing literature on the implementation of peer support interventions and identify barriers and facilitators using an implementation framework. The Consolidated Framework for Implementation Research (CFIR) was used to organize the literature obtained in the systematic search and synthesize best practices for implementation. The systematic search identified 19 published articles that were coded for relevant information including implementation barriers and facilitators. The review highlighted a number of important elements for implementation within the CFIR domains, including clear role definition, a flexible organizational culture, and education for peer and nonpeer staff. Implementation barriers included an organizational culture without a recovery focus, allied practitioners' beliefs about peer support, and an unclear peer role. The results of this review provide a summary of best practices for the implementation of peer support in mental health services that can be used by researchers and service providers in future implementation. These practices should continue to be tested and reworked as the climate of recovery-oriented services within mental health organizations evolves. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".