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Record W4415649870 · doi:10.1080/08974454.2025.2576210

The Role of Child Maltreatment and Revictimization on Substance Use Trajectories Among Women Involved in the Criminal Justice System

2025· article· en· W4415649870 on OpenAlexaff
Abenaa Jones, Sienna Strong-Jones, Rachael E. Bishop, Kristina Brant, Jacquelyn C. Campbell

Bibliographic record

VenueWomen & Criminal Justice · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsCanadian Centre on Substance Use and Addiction
FundersNational Center for Advancing Translational SciencesNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug AbuseNational Institutes of HealthStrongSocial Science Research Institute, Pennsylvania State UniversityPennsylvania State UniversityUniversity of Pennsylvania
KeywordsSubstance useCriminal justiceSubstance abusePoison controlDomestic violenceEconomic JusticeHuman factors and ergonomicsChild custody

Abstract

fetched live from OpenAlex

We explored the mechanisms underlying the relationship between trauma in childhood and adulthood and substance use initiation and recovery from opioid use disorder (OUD). We triangulated the perspectives of 20 women with current/previous OUD and 22 criminal legal and drug treatment professionals. Themes centered around the long-term effects of unaddressed childhood trauma, family- and partner-based trauma across the life course, sexual trauma, and traumatic events due to opioid use. Findings highlighted the unique trauma-related needs of women involved in the criminal legal system who also use drugs. Approaches to care that are gender-specific, person-centered, and trauma-informed would facilitate retention in drug treatment programs.

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)0.005
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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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