How did the tobacco ban increase inmates’ body weight during incarceration in Canadian federal penitentiaries? A cohort study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine how inmates' body weight changed during incarceration in Canadian federal penitentiaries, based on their history of tobacco use. Since tobacco was banned from all Canadian federal penitentiaries in 2008, little is known about the unintended health consequences of this ban, especially on inmates' body weight. DESIGN: Cohort study. SETTING: Participants were male and female inmates incarcerated for at least 6 months in Canadian federal penitentiaries. We collected data from 10 institutions in two Canadian regions (Ontario and Atlantic). PARTICIPANTS: We collected data from 754 inmates who volunteered to participate in the study. INTERVENTION: This study examined weight change in relation to a history of tobacco use. In 2016-2017, anthropometric data were collected and compared with recorded anthropometric data at the beginning of incarceration (mean follow-up of 5.0±8.3 years). Self-reported data on tobacco and substance use were collected. Weight change was compared between inmates with and without a history of tobacco use. OUTCOMES: ), annual weight change (kg/year), and BMI and waist circumference (cm) at the time of the interview. RESULTS: During incarceration, ex-smokers gained more than twice the amount of weight compared with non-smokers (7.5 kg weight gain for smokers vs 3.7 kg weight gain for non-smokers). Once adjusted for covariates in a regression analysis, for inmates who gained the most weight (75th and 90th percentiles), non-smokers had, respectively, 1.64 and 2.3 lower BMI points than ex-smokers. CONCLUSIONS: During incarceration in Canadian federal penitentiaries, inmates with a history of tobacco use gained significantly more weight than non-smokers. This put them at increased risk of developing obesity-related health problems. This information is important for the prison setting when planning related programmes and regulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".