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Record W2903136803 · doi:10.22215/etd/2018-12892

A Meta-Analysis of Substance Misuse Intervention Programs Offered to Women Offenders

2018· dissertation· en· W2903136803 on OpenAlexafffund
Chealsea De Moor

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecidivismPsychological interventionOddsMeta-analysisPsychologyDriving under the influenceOdds ratioClinical psychologyIntervention (counseling)PsychiatryPoison controlHuman factors and ergonomicsMedicineEnvironmental healthLogistic regression

Abstract

fetched live from OpenAlex

This meta-analytic review examined the effectiveness of substance misuse interventions in reducing recidivism and substance use outcomes among women offenders.A literature search revealed 22 evaluations, reporting 39 effect sizes.The effect estimate for recidivism outcomes revealed 53% to 79% reductions in the odds of recidivism for women participating in interventions.Effect estimates were similar for outcomes relating to substance use, with reductions in the odds of alcohol and drug use ranging from 13% to 82%.Across evaluations reporting recidivism outcomes, gender-neutral and gender-informed interventions were equally effective in reducing recidivism outcomes.Further, sub-group analyses revealed that study quality did not impact reductions in recidivism.Overall, this review lends support to the findings of previous research, suggesting that substance misuse interventions are effective in reducing both recidivism and substance use outcomes among women offenders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.026
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.140
GPT teacher head0.390
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations0
Published2018
Admission routes2
Has abstractyes

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