MétaCan
Menu
Back to cohort
Record W3138426398 · doi:10.1089/jchc.19.04.0039

Weight Gain and Mental Health in the Canadian Prison Population

2021· article· en· W3138426398 on OpenAlexaffabout
Claire Johnson, Jean‐Philippe Chaput, Amélie Blanchard, Lise Dubois

Bibliographic record

VenueJournal of Correctional Health Care · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsChildren's Hospital of Eastern OntarioUniversité de MonctonUniversity of OttawaInstitute of Population and Public Health
Fundersnot available
KeywordsMedicineWeight gainOverweightPsychotropic medicationBody mass indexPrisonPsychiatryMental healthMental illnessObesityPopulationMass incarcerationWeight changeBody weightWeight lossEnvironmental healthInternal medicinePsychology

Abstract

fetched live from OpenAlex

Most inmates gain excessive bodyweight during incarceration in Canadian federal penitentiaries. It is currently unknown if the weight gain is related to participants' higher prevalence of mental illness and/or psychotropic medication use. This study examined how weight change (kg) and body mass index (BMI) change (kg/m2) of 1,420 participants were associated with mental health status and psychotropic medication use. Participants who took psychotropic medications did not gain more weight during incarceration compared to their counterparts who were not taking psychotropic medications (6.5 kg vs. 6.0 kg, p = 0.87, respectively). However, participants taking psychotropic medications were more likely to be overweight or obese, which means they already had higher BMI at the beginning of their incarceration as opposed to gaining more weight during incarceration. Weight gain of participants observed during incarceration in Canadian federal penitentiaries was not related to the higher prevalence of mental illness or psychotropic medication use.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.018
GPT teacher head0.354
Teacher spread0.336 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Correctional Health CareSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207