Weight gain and social support networks in Canadian federal correctional facilities
Bibliographic record
Abstract
Purpose This study aims to examine whether inmate’s social support network is related to changes in anthropometric data among individuals in Canadian correctional facilities. Design/methodology/approach Methods: A total of 754 participants in federal correctional facilities who had been incarcerated for at least six months responded to the questionnaire by interview regarding their social support network. Chi-square tests and non-parametric tests for median comparison were used to measure changes in anthropometric data [weight and body mass index (BMI)] between the date of admission into custody and the date of the interview. Subsequently, a multivariate regression analysis for BMI change was conducted to adjust for covariates such as sex, age and ethnicity. Findings Results: Participants who received more than two visits per month had significantly lower weight gain (2.6 kg) than those who received less than one visit per month (7.1 kg, p = 0.02). Similar results were observed for the average change in BMI (p = 0.01). The influence of an external social support network on BMI change remained significant after adjusting for covariates. Conclusions: An individual's external social support network (outside the prison environment) may protect against weight gain in correctional facilities. Given how social support will vary based on the prison context by country and jurisdiction, individual and organizational strategies should be considered to maintain a healthy social support network and increase the number of visits (at every stage of incarceration) to counteract this weight gain and its adverse health consequences. Originality/value The social support network outside the prison environment may protect against weight gain in correctional facilities. Strategies should be considered to maintain a healthy social support network and increase the number of visits to counteract this weight gain and its adverse health consequences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".