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Record W4387765183 · doi:10.1111/add.16365

Using the Alcohol, Smoking and Substance Involvement Screening Test to predict substance‐related hospitalisation after release from prison: A cohort study

2023· article· en· W4387765183 on OpenAlexaff
Craig Cumming, Stuart A. Kinner, Rebecca McKetin, Jesse T Young, Ian Li, David B. Preen

Bibliographic record

VenueAddiction · 2023
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Health and Medical Research Council
KeywordsCannabisMedicinePrisonSubstance dependenceAlcohol Use Disorders Identification TestCohortPsychiatryCohort studyProportional hazards modelHazard ratioAddictionPoison controlInternal medicinePsychologyEmergency medicineInjury preventionConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Poor substance use-related health outcomes after release from prison are common. Identifying people at greatest risk of substance use and related harms post-release would help to target support at those most in need. The Alcohol Smoking and Substance Involvement Screening Test (ASSIST) is a validated substance use screener, but its utility in predicting substance-related hospitalisation post-release is unestablished. We measured whether screening for moderate/high-risk substance use on the ASSIST was associated with increased risk of substance-related hospitalisation. DESIGN: A prospective cohort study. SETTING: Prisons in Queensland and Western Australia. PARTICIPANTS: Participants were incarcerated and within 6 weeks of expected release when recruited. A total of 2585 participants were followed up for a median of 873 days. MEASUREMENTS: Baseline survey data were combined with linked unit record administrative hospital data. We used the ASSIST to assess participants for moderate/high-risk cannabis, methamphetamine and heroin use in the 3 months prior to incarceration. We used International Classification of Diseases (ICD) codes to identify substance-related hospitalisations during follow-up. We compared rates of substance-related hospitalisation between those classified as low/no-risk and moderate/high-risk on the ASSIST for each substance. We estimated adjusted hazard ratios (aHR) by ASSIST risk group for each substance using Weibull regression survival analysis allowing for multiple failures. FINDINGS: During follow-up, 158 (6%) participants had cannabis-related, 178 (7%) had opioid-related and 266 (10%) had methamphetamine-related hospitalisation. The hazard rates of substance-related hospitalisation after prison were significantly higher among those who screened moderate/high-risk compared with those screening low risk on the ASSIST for cannabis (aHR 2.38, 95% confidence interval [CI] 1.74, 3.24), methamphetamine (aHR 2.23, 95%CI 1.75, 2.84) and heroin (aHR 5.79, 95%CI 4.41, 7.60). CONCLUSIONS: Incarcerated people with an Alcohol Smoking and Substance Involvement Screening Test (ASSIST) screening of moderate/high-risk substance use appear to have a significantly higher risk of post-release substance-related hospitalisation than those with low risk. Administering the ASSIST during incarceration may inform who has the greatest need for substance use treatment and harm reduction services in prison and after release from prison.

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)0.000
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 score0.004
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.041
GPT teacher head0.288
Teacher spread0.247 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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