Guidelines for peer support in high‐risk organizations: An international consensus study using the delphi method
Bibliographic record
Abstract
Despite widespread adoption of peer-support programs in organizations around the world whose employees are at high risk of exposure to potentially traumatic incidents, little consensus exists regarding even the most basic concepts and procedures for these programs. In this article, consensus refers to a group decision-making process that seeks not only agreement from most participants, but also resolution of minority objections. The aim of the current study was to develop evidence-informed peer-support guidelines for use in high-risk organizations, designed to enhance consistency around goals and procedures and provide the foundation for a systematic approach to evaluation. From 17 countries, 92 clinicians, researchers, and peer-support practitioners took part in a 3-round web-based Delphi process rating the importance of statements generated from the existing literature. Consensus was achieved for 62 of 77 (81%) statements. Based upon these, 8 key recommendations were developed covering the following areas: (a) goals of peer support, (b) selection of peer supporters, (c) training and accreditation, (d) role of mental health professionals, (e) role of peer supporters, (f) access to peer supporters, (g) looking after peer supporters, and (h) program evaluation. This international consensus may be used as a starting point for the design and implementation of future peer-support programs in high-risk organizations.
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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 | 0.256 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".