Extending the peer support specialist pathway for supporting recovery
Bibliographic record
Abstract
Recovery Corps is a pioneering social innovation in behavioural health, addressing the critical need for peer-driven recovery support services amidst the ongoing substance use crisis in the United States. Leveraging AmeriCorps infrastructure, Recovery Corps recruits and trains individuals with substance use disorder (SUD) lived experience to provide peer support in underserved communities. This narrative examines central assumptions associated with the Recovery Corps initiative, including those related to a perceived unmet demand for peer support, the feasibility of training community members without professional backgrounds, the impact of Recovery Corps peer support on recovery outcomes, and the degree to which Recovery Corps experience creates career pathways for individuals in recovery. By bridging service gaps, enhancing recovery capital, and fostering sustainable workforce development, Recovery Corps offers a comprehensive model for integrating peer support within behavioural health frameworks. Lessons drawn from Recovery Corps underscore the importance of capacity building, flexible evaluation methods, and strategic partnerships to sustain and scale peer-driven interventions. This program highlights an adaptable approach to recovery support, presenting a model that may inform future social innovation in behavioural health.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".