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Record W4415356609 · doi:10.1080/07448481.2025.2573107

Who participates in collegiate recovery programs? A survey of students in the US and Canada

2025· article· en· W4415356609 on OpenAlexaboutno aff
Noel Vest, Michelle Flesaker, Christine Timko, Keith Humphreys, Michael D. Stein, Tabor Hoatson, John F. Kelly

Bibliographic record

VenueJournal of American College Health · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institute on Alcohol Abuse and Alcoholism
KeywordsMental healthAnxietyStigma (botany)Harm reductionDepression (economics)HarmCollege healthHealth equityPopulation

Abstract

fetched live from OpenAlex

Objective: To characterize the demographics, recovery pathways, and support needs of students enrolled in Collegiate Recovery Programs (CRPs) across the U.S. and Canada. Participants: 246 students from 77 institutions who were currently enrolled in CRPs during the 2023–2024 academic year. Methods: Students completed an online survey assessing demographics, recovery history, mental health diagnoses, academic status, and use of on- and off-campus recovery supports. Results: Only 54.1% reported prior formal SUD treatment. Nearly half of participants identified as LGBTQIA+, and one-third reported justice system involvement. Most had co-occurring mental health conditions, particularly depression (79.3%) and anxiety (76.8%). Recovery pathways included 12-step programs (57.3%), counseling (53.3%), CRP only (47.6%), and harm reduction (22.0%). Conclusions: CRP participants reflect a diverse population with complex and evolving needs. Findings underscore the importance of inclusive, flexible recovery supports and highlight CRPs’ role in advancing health equity and reducing stigma in college settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.380
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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