Growing the peer workforce in rural mental health and social and emotional well‐being services: A scoping review of the literature
Bibliographic record
Abstract
INTRODUCTION: Growing the mental health peer workforce holds promise for rural communities, but we currently lack an understanding of the guidance available to support the development, implementation and sustainability of this workforce in rural settings. OBJECTIVE: Study aims are to: (1) determine the extent and nature of the literature that provides guidance for growing the peer workforce in rural mental health services; and (2) identify and explore any guidance relevant to rural peer work services dedicated to First Nations communities, including those promoting social and emotional well-being within this body of literature. DESIGN: A scoping review method was employed to identify relevant peer-reviewed and grey literature published between 2013 and 2022 across PsychInfo, Medline, Embase and CINAHL, Scopus and Informit HealthInfoNet databases, as well as targeted organisation websites and Google Advanced Search. FINDINGS: A total of 26 unique studies/projects were included from the US, UK, Canada and Australia with public mental health, non-government/for purpose and private sector service settings represented in the literature. Grey literature, such as reports of evaluations and frameworks, formed the majority of included texts. While there is a lesser volume of rurally focused literature relative to the general peer work literature, this is a rich body of knowledge, which includes guidance concerning services dedicated to First Nations communities. Via synthesis critical considerations were identified for the development, implementation and sustainability of peer work in rural mental health services across six domains: 'Working with community members and stakeholders', 'Organisational culture and governance', Working with others and in teams, Professional expertise and experience, Being part of and working in the community and 'Local mental health services capacity'. DISCUSSION: While there are considerations relevant across a range of settings, the domains of: 'working with community members and stakeholders', 'being part of and working in the community' and 'local mental health services capacity', capture additional, distinct and nuanced challenges and opportunities for growing the peer work in rural services. CONCLUSION: The literature offers insights valuable for service planning, policy development and the allocation of resources to support rural peer workforce growth.
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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 | 0.036 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 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".