Actionable Items to Address Challenges Incorporating Peer Support Specialists Within an Integrated Mental Health and Substance Use Disorder System: Co-Designed Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Peer support specialists offering mental health and substance use support services have been shown to reduce stigma, hospitalizations, and health care costs. However, as peer support specialists are part of a fast-growing mental health and substance use workforce in innovative integrated care settings, they encounter various challenges in their new roles and tasks. OBJECTIVE: The purpose of this study was to explore peer support specialists' experiences regarding employment challenges in integrated mental health and substance use workplace settings in New Hampshire, USA. METHODS: Using experience-based co-design, nonpeer academic researchers co-designed this study with peer support specialists. We conducted a series of focus groups with peer support specialists (N=15) from 3 different integrated mental health and substance use agencies. Audio recordings were transcribed. Data analysis included content analysis and thematic analysis. RESULTS: We identified 90 final codes relating to 6 themes: (1) work role and boundaries, (2) hiring, (3) work-life balance, (4) work support, (5) challenges, and (6) identified training needs. CONCLUSIONS: The shared values of experience-based co-design and peer support specialists eased facilitation between peer support specialists and nonpeer academic researchers, and indicated that this methodology is feasible for nonpeer academic researchers and peer support specialists alike. Participants expressed challenges with agency restrictions, achieving work-life balance, stigma, and low compensation. We present actionable items to address these challenges in integrated mental health and substance use systems to potentially offset workforce dissatisfaction and high turnover rates.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".