Characterizing Collegiate Recovery Programs in the United States and Canada: A Survey of Program Directors
Bibliographic record
Abstract
OBJECTIVE: Collegiate recovery programs (CRPs) play a vital and expanding role in supporting students in recovery from substance use disorders and behavioral addictions at higher education institutions. Despite their importance, there is a lack of comprehensive research characterizing CRPs, including their program directors and the nature and influence of their funding streams. METHOD: A survey was administered to 70 CRP directors across the United States and Canada. Directors reported on their CRPs across a variety of site physical features, policy and program offerings, and funding sources, with "sustainable" funding defined as coming from two or more sources. RESULTS: CRP directors were predominantly non-Hispanic, White, and female, with representation from gender and sexual minorities. The highest concentration of directors responding was in the U.S. states of North Carolina, California, and Texas. CRPs with more than one funding source served twice as many students and had significantly more space, drop-in facilities, and relapse management policies. CRP directors had positive perceptions of harm-reduction principles. In addition, results highlighted the robust availability of All Recovery meetings and the wide diversity of mutual-help group meetings offered within CRPs, as well as the directors' positive perceptions of these meetings. CONCLUSIONS: This research lays a foundation for enhancing CRPs within higher education settings, emphasizing the significance of sustained funding and an inclusive support framework for the program directors who run CRPs. Future studies should further explore the effectiveness of CRPs and their impacts on the schools and students they serve.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".