Weight gain and chronic disease progression among individuals incarcerated in Canadian federal penitentiaries: a retrospective cohort study
Bibliographic record
Abstract
Purpose Very little is known about how weight gain during incarceration influences the health of people living in Canadian federal penitentiaries. To fill this knowledge gap, this study aims to determine how the observed weight gain influenced the development of obesity-related chronic diseases during incarceration. Design/methodology/approach This retrospective cohort study examined the association between weight gain and obesity-related chronic diseases for 1,420 participants incarcerated in federal penitentiaries in Ontario, New Brunswick and Nova Scotia. To participate, individuals had to be incarcerated for at least six months at the time of the study (2016–2017). Current anthropometric data were measured or taken from medical records, then compared to anthropometric data at the beginning of incarceration (mean follow-up of 5.0 years) to determine weight change (kg) and body mass index change (kg/m 2 ) during incarceration. Then, information about obesity-related chronic diseases was drawn from the participants’ medical records. Findings Chi-square and nonparametric median comparison tests were performed to detect statistically significant changes in anthropometric data, to determine if a relationship was present. This study observed a significant association between weight gain and disease development for many types of obesity-related chronic diseases (e.g. cancer, type 2 diabetes, hypertension, dyslipidemia and sleep apnea). This confirmed an association between weight gain and chronic disease development in the prison population. Originality/value Participants who gained a significant amount of weight, during incarceration, were also more frequently diagnosed with obesity-related chronic diseases. These findings suggest that weight gain may contribute to the deterioration of peoples’ health during incarceration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".