Carrying the Pain of Abuse: Gender-Specific Findings on the Relationship between Childhood Physical Abuse and Obesity in Adulthood
Bibliographic record
Abstract
BACKGROUND: Childhood abuse has been associated with negative adult health outcomes, including obesity. This study sought to investigate the association between childhood physical abuse and adult obesity, while controlling for five clusters of potentially confounding factors: childhood stressors, socioeconomic indicators, marital status, health behaviors, and mental health. METHODS: Representative data from the 2005 Canadian Community Health Survey were selected. The response rate was approximately 84%. Gender-specific logistic regression analyses determined the association between abuse and obesity, while controlling for age and race and five clusters of potentially confounding factors. Of the 12,590 respondents with complete data, 2,787 were obese and 976 reported physical abuse as a child or adolescent by someone close to them. RESULTS: Among women with childhood physical abuse compared to no abuse, the odds of obesity were 35% higher, even when controlling for age, race, and the five clusters of factors (odds ratio (OR) = 1.35; 95% confidence interval (CI) = 1.09, 1.67). Childhood physical abuse was not associated with adult obesity among men (OR = 1.12; 95% CI = 0.82, 1.53). CONCLUSIONS: This study provides one of the first population-based, gender-specific analyses of the association between childhood physical abuse and obesity controlling for a wide range of factors. The gender-specific findings require further exploration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".