Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is one of the most serious health challenges facing women today. Investigations into CVD risk factors specific to women have focused primarily on sex-based differences, with little attention paid to gender-based influences. Abuse, such as child abuse, intimate partner violence, and sexual assault, is a serious gendered issue affecting one quarter to one-half of all women within their lifetime. Despite beginning evidence that abuse may increase CVD risk in women, the biological, behavioral, and psychological pathways linking abuse to CVD have received little attention from researchers and clinicians. PURPOSE: The aim of this study was to propose a conceptual model that delineates the pathways by which abuse may increase CVD risk among women. Within the model, lifetime abuse is positioned as a chronic stressor affecting CVD risk through direct and indirect pathways. Directly, abuse experiences can cause long-term biophysical changes within the body, which increase the risk of CVD. Indirectly, smoking and overeating, known CVD risk behaviors, are common coping strategies in response to abuse. In addition, women with abuse histories frequently report depressive symptoms, which can persist for years after the abusive experience. Depressive symptoms are a known predictor of CVD and can potentiate CVD risk behaviors. Therefore, depressive symptoms are proposed as a mediator between lifetime abuse and CVD as well as between lifetime abuse and CVD risk behaviors. CONCLUSIONS AND CLINICAL IMPLICATIONS: To better promote cardiovascular health among women and direct appropriate interventions, nurses need to understand the complex web by which abuse may increase the risk for CVD. In addition, nurses need to not only pay attention to an abuse history and symptoms of depression for women presenting with CVD symptoms but also address CVD risk among women with abusive histories.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".