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Record W2888853348 · doi:10.9778/cmajo.20180044

Canadian federal penitentiaries as obesogenic environments: a retrospective cohort study

2018· article· en· W2888853348 on OpenAlexaffvenueabout
Claire Johnson, Jean‐Philippe Chaput, Maikol Diasparra, Catherine Richard, Lise Dubois

Bibliographic record

VenueCMAJ Open · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsOttawa Public HealthChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineBody mass indexRetrospective cohort studyObesityDemographyWeight gainCohortConfidence intervalAnthropometryCohort studyWeight changeWeight lossBody weightInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Very little is known about how incarceration influences a person's weight in Canada. We sought to determine how inmates' weights change during their incarceration in Canadian federal penitentiaries. METHODS: We performed a retrospective, longitudinal cohort study to examine weight change in Canadian federal penitentiaries. To participate, inmates had to have been incarcerated for at least 6 months at the time of the study. Current anthropometric data were measured or taken from medical records, then compared with anthropometric data from the beginning of incarceration (mean follow-up of 5.0 ± 8.3 yr). We examined 3 outcomes: change in weight (kg), change in body mass index (BMI) and rate of weight change (kg/yr) during incarceration. RESULTS: = 645). The proportion of inmates with a BMI in the normal range (18.5-24.9) decreased by 52%. Weight gain was found to be associated with older age, region (Ontario v. Atlantic), ethnicity (Aboriginal inmates showed the highest weight gain), longer incarceration, and longer total sentence. However, weight gain was not associated with sex, feeding system or spoken language. INTERPRETATION: The Canadian correctional environment can be considered obesogenic, with most inmates experiencing undesirable and rapid weight gain during their incarceration. Rates of obesity increased dramatically during incarceration, and could put inmates at increased risk of obesity-related health problems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.316
Teacher spread0.297 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2018
Admission routes3
Has abstractyes

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