Childhood maltreatment as a risk factor for diabetes: findings from a population-based survey of Canadian adults
Bibliographic record
Abstract
BACKGROUND: It is well established that childhood maltreatment (CM) is a risk factor for various mental and substance use disorders. To date, however, little research has focused on the possible long-term physical consequences of CM. Diabetes is a chronic disease, for which an association with CM has been postulated. METHODS: Based on data from a sample of 21,878 men and women from the 2012 Canadian Community Health Survey - Mental Health (CCHS - MH), this study examines associations between three types of CM (childhood physical abuse (CPA), childhood sexual abuse (CSA), and childhood exposure to intimate partner violence (CEIPV)) and diabetes in adulthood. Multiple logistic regression models were used to examine associations between CM and diabetes controlling for the effects of socio-demographic characteristics and risk factors for type 2 diabetes. RESULTS: When controlling socio-demographic characteristics, diabetes was significantly associated with reports of severe and frequent CPA (OR = 1.8) and severe and frequent CSA (OR = 2.2). A dose-response relationship was observed when co-occurrence of CSA and CPA was considered with the strongest association with diabetes being observed when both severe and frequent CSA and CPA were reported (OR = 2.6). Controlling for type 2 diabetes risk factors attenuated associations particularly for CPA. CEIPV was not significantly associated with having diabetes in adulthood. CONCLUSION: CPA and CSA are risk factors for diabetes. For the most part, associations between CPA and diabetes are mediated via risk factors for type 2 diabetes. Failure to consider severity and frequency of abuse may limit our understanding of the importance of CM as a risk factor for diabetes.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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".