Emotion Regulation and Favorable Cardiovascular Health Among Women
Bibliographic record
Abstract
OBJECTIVE: Emerging evidence suggests maladaptive (eg, suppression) versus adaptive (eg, reappraisal) emotion regulation strategies predict the risk of cardiovascular disease, which is the leading cause of death among women. Recognizing that health encompasses more than the absence of disease, we investigated whether reappraisal and suppression strategies are individually or jointly related to favorable cardiovascular health (CVH) over time among women. METHODS: At baseline in 2017-2018, 28,759 postmenopausal women free of cardiovascular disease (age mean =63) from the Nurses' Health Study II cohort answered the validated Emotion Regulation Questionnaire assessing reappraisal and suppression strategies. Favorable CVH was defined at baseline and in 2019 based on self-reported diagnoses of hypertension, cholesterol, and diabetes, as well as body mass index and smoking status. Poisson regression models evaluated the relative risk (RR) and 95% CI of having favorable CVH in 2019 related to baseline use of each emotion regulation strategy and their interplay, considering relevant covariates. RESULTS: In sociodemographic-adjusted models, greater reappraisal use was related to a higher likelihood of having favorable CVH (RR per 1-SD increase =1.07, 95% CI=1.06-1.09), while greater suppression use was related to a lower likelihood (RR per 1-SD increase =0.96, 95% CI=0.94-0.97). Relative to women reporting lower use of both strategies, those using both strategies frequently (RR=1.13, 95% CI=1.08-1.19) and those favoring reappraisal over suppression use (RR=1.21, 95% CI=1.14-1.27) had a higher likelihood of favorable CVH. Associations were attenuated, but most remained evident after adjusting for baseline CVH and health behaviors. CONCLUSIONS: Use of adaptive versus maladaptive emotion regulation strategies predicts the likelihood of having favorable CVH in expected directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".