What do peer support workers do? A job description
Bibliographic record
Abstract
BACKGROUND: The extant literature suggests that poorly defined job roles make it difficult for peer support workers to be successful, and hinder their integration into multi-disciplinary workplace teams. This article uses data gathered as part of a participatory evaluation of a peer support program at a psychiatric tertiary care facility to specify the work that peers do. METHODS: Data were gathered through interviews, focus groups, and activity logs and were analyzed using a modified grounded theory approach. RESULTS: Peers engage in direct work with clients and in indirect work that supports their work with clients. The main types of direct work are advocacy, connecting to resources, experiential sharing, building community, relationship building, group facilitation, skill building/mentoring/goal setting, and socialization/self-esteem building. The main types of indirect work are group planning and development, administration, team communication, supervision/training, receiving support, education/awareness building, and information gathering and verification. In addition, peers also do work aimed at building relationships with staff and work aimed at legitimizing the peer role. Experience, approach, presence, role modeling, collaboration, challenge, and compromise can be seen as the tangible enactments of peers' philosophy of work. CONCLUSIONS: Candidates for positions as peer support workers require more than experience with mental health and/or addiction problems. The job description provided in this article may not be appropriate for all settings, but it will contribute to a better understanding of the peer support worker position, the skills required, and the types of expectations that could define successful fulfillment of the role.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".