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Record W4415368994 · doi:10.1080/14789949.2025.2576177

The influence of social and environmental factors on drug use in female prisoners

2025· article· en· W4415368994 on OpenAlexaboutno aff
Louis Favril

Bibliographic record

VenueJournal of Forensic Psychiatry and Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersFonds Wetenschappelijk Onderzoek
KeywordsPrisonDrugCannabisPerspective (graphical)Quarter (Canadian coin)Multivariate analysisSocial environmentQualitative research

Abstract

fetched live from OpenAlex

Limited quantitative evidence exists for the potential impact of social and environmental factors on drug use during imprisonment. Self-report data were collected from 211 adult women (88% response rate) in Belgian prisons, representing 42% of all female prisoners nationwide. During their current incarceration, one in three (31%) participants had ever used drugs and a quarter (26%) did so in the past month. Cannabis and non-prescribed tranquillizers (such as benzodiazepines) were most commonly used. The main reasons cited by participants for using drugs in prison were to relieve stress, forget problems, and counteract boredom. In a multivariate analysis, perceived social support and availability of meaningful activities were negatively associated with recent drug use while incarcerated. Other factors related to the prison environment had no significant influence on drug use, providing a more nuanced perspective on extant qualitative literature.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.321
Teacher spread0.305 · 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 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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