Input from stakeholders: Hiring and retaining people in Recovery in the behavioral health workforce
Bibliographic record
Abstract
The hiring of people in recovery (PIR) from mental illnesses in the behavioral health workforce has bloomed in the past few years. PIR have been hired in a variety of positions including as peer supporters, case managers, clinicians, and CEOs. This paper highlights findings from focus groups that were conducted with behavioral health organizations to gather input on hiring and retaining PIRs in the behavioral health workforce. Suggestions were mostly targeted to the areas of 1) Hiring Peers— the value and benefits and challenges, and suggestions for organizational readiness and, 2) Retaining PIRs—transitioning PIRs to the behavioral health workforce and supervision, flexibility and accommodations. Input from these discussions has informed the creation of development and implementation guides for organizations wanting to hire PIRs as peer supporters. In addition to the development of training guidelines for PIRs in roles as peer supporters. This paper signifies the importance of gathering input prior to developing implementation initiatives on the employment of PIRs as peer supporters, in the behavioral health workforce.
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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 | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".