Gender Differences in the Association between Parental Divorce during Childhood and Stroke in Adulthood: Findings from a Population-Based Survey
Bibliographic record
Abstract
BACKGROUND: Although there is a substantial literature examining the mental health consequences of parental divorce, less attention has been paid to possible long-term physical health outcomes. AIMS: The aim of this study was to examine the gender-specific association between childhood parental divorce and later incidence of stroke, while controlling for age, race ethnicity, socioeconomic status, health behaviors, diabetes, social support, marital status, mental health, and health care utilization. METHODS: Secondary analysis of the population-based Behavioral Risk Factor Surveillance System survey; logistic regression analyses were conducted. The final sample included 4074 males and 5886 females. Respondents were excluded if they had experienced parental addictions to drugs or alcohol, any form of childhood abuse (physical, sexual, or emotional), or witnessed domestic violence. RESULTS: A threefold risk of stroke was found for males who had experienced parental divorce before the age of 18 in comparison with males whose parents had not divorced [age- and race ethnicity-adjusted model odds ratio (OR) = 2·99, 95% confidence interval (CI) = 1·79, 4·98; fully adjusted model OR = 3·01, 95% CI = 1·68, 5·39]. Parental divorce was not significantly associated with stroke among women (fully adjusted OR = 1·64, 95% CI = 0·89, 3·02). CONCLUSIONS: There is a robust association between parental divorce and stroke among males, even after adjustment for many known risk factors and the exclusion of respondents who had experienced parental addictions or family violence. Further research is needed to investigate plausible pathways linking parental divorce and stroke in males.
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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 | 0.000 | 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) | 0.001 | 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".