Toward consensus in mental health peer support training: a co-created Delphi study
Bibliographic record
Abstract
Despite the increasing recognition of peer support in mental health services, standardization of training programmes remains underdeveloped, with limited consensus on essential training elements. This co-created study aimed to establish consensus on elements of peer support training. The study was conducted by researchers and members of peer support associations. A review of 64 peer support training programmes informed the development of a preliminary list of training elements and features. Then 73 stakeholders were invited to two rounds of Delphi consultations to rate the importance of training elements and related factors. Delphi consultations highlighted essential topics for training, i.e. self-disclosure practices and boundary-setting. Educational institutions were strongly endorsed as accrediting bodies for training programmes. Key attributes for peer support workers to be hired were identified, although none reached formal consensus. Recommended duration of training was 27 training sessions on average, with considerable variation in session duration. The study provides a comprehensive framework for peer support training, recommending structured, modular, and accredited training programmes to professionalize and sustain peer support services. Future research should validate these findings in diverse cultural and healthcare contexts and explore the long-term impact of such training programmes on peer support practice and outcomes.
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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 | 0.146 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".