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Record W2077671952 · doi:10.2105/ajph.2014.302498

A Systematic Review of Randomized Controlled Trials of Interventions to Improve the Health of Persons During Imprisonment and in the Year After Release

2015· review· en· W2077671952 on OpenAlexafffund
Fiona G. Kouyoumdjian, Kathryn E. McIsaac, Jessica Liauw, Samantha Green, Fareen Karachiwalla, Winnie Siu, Kaite Burkholder, Ingrid A. Binswanger, Lori Kiefer, Stuart A. Kinner, Mo Korchinski, Flora I. Matheson, Pam Young, Stephen W. Hwang

Bibliographic record

VenueAmerican Journal of Public Health · 2015
Typereview
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsImprisonmentPsychological interventionRandomized controlled trialMedicineMental healthMEDLINEGerontologyPsychiatryPsychologyCriminologyInternal medicine

Abstract

fetched live from OpenAlex

We systematically reviewed randomized controlled trials of interventions to improve the health of people during imprisonment or in the year after release. We searched 14 biomedical and social science databases in 2014, and identified 95 studies. Most studies involved only men or a majority of men (70/83 studies in which gender was specified); only 16 studies focused on adolescents. Most studies were conducted in the United States (n = 57). The risk of bias for outcomes in almost all studies was unclear or high (n = 91). In 59 studies, interventions led to improved mental health, substance use, infectious diseases, or health service utilization outcomes; in 42 of these studies, outcomes were measured in the community after release. Improving the health of people who experience imprisonment requires knowledge generation and knowledge translation, including implementation of effective interventions.

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)0.018
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.094
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.010
Bibliometrics0.0120.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.087
GPT teacher head0.449
Teacher spread0.362 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations126
Published2015
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Public HealthSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207