Canadian federal penitentiaries as obesogenic environments: a retrospective cohort study
Bibliographic record
Abstract
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: A total of 1420 inmates participated in this study. Almost three-quarters (73.0%, n = 1037)) of participants gained weight during incarceration. Inmates gained a median of 6.2 (95% confidence interval [CI] 5.6–6.9) kg, and BMI increased by 2.0 (95% CI 1.8–2.2). Obesity rates increased by 71%, from 26.6% of participants (n = 378) on admission to 45.4% of participants at follow-up (n = 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".