Modeling lifetime abuse and cardiovascular disease risk among women
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is one of the most significant health challenges facing women today. Abuse is a serious gendered issue also affecting the health of women. Despite beginning evidence that abuse may increase the risk of CVD among women, causal pathways linking abuse to CVD have received little attention. Our purpose was to test Scott-Storey's conceptual model showing direct and indirect pathways through which lifetime abuse severity may affect women's CVD risk. METHODS: Using data collected from a community sample of 227 Canadian women who had left an abusive partner, we conducted structural equation modeling with latent growth curve analysis using a phantom variable approach to test the direct effects of severity of lifetime abuse on CVD risk (indicated by measures of systolic and diastolic blood pressure) as well as its indirect effects through CVD risk behaviors and through women's initial level of depressive symptoms and the observed rate of change in their depressive symptoms over time. RESULTS: Women in this sample had above average CVD risk factors (i.e., smoking, overweight/obesity, depressive symptoms, high blood pressure) in comparison to women in the general population. Further, CVD risk behaviors increased with severity of lifetime abuse and remained present long after leaving the abusive relationship. Results of the tested model provide preliminary evidence supporting many of the hypothesized pathways by which severity of lifetime abuse can increase CVD risk among women; the model fit the data reasonably well explaining 41% of the variance in CVD risk. CONCLUSIONS: Findings support the growing recognition of the long-term effects of lifetime abuse on cardiovascular health, suggest important implications for clinicians working with women, and provide a novel approach for studying the concept of cumulative lifetime abuse through the use of a phantom variable.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".