"I'm at risk for heart disease? " self-compassion and reactions to a health threat
Bibliographic record
Abstract
Self-compassion (SC), treating oneself kindly in the face of setbacks, can decrease negative emotions and promote health behaviours. SC may mitigate negative emotions about being at-risk for cardiovascular disease (CVD) and may promote adaptive health behaviour self-regulation. No research has examined SC in the context of CVD. We examined SC's relationship with responses to CVD risk. Participants were 102 women (Mage = 66.50, SD = 6.04), with a CVD risk score (Rassmussen criteria) of 3 or higher. Participants met with a research assistant on three occasions. First, baseline levels of physical activity (PA) were measured. Second, participants' level of SC, health-promoting lifestyle habits, illness self-blame and immediate reactions to news of being moderate to high risk of CVD were measured (negative affect). One week later, PA, negative affect and health behaviours were measured. Semi-partial correlations, controlling for self-esteem, revealed baseline SC was correlated to self-report PA (r = .24, p = .01), nutrition (r = .28, p = .02), stress management (r = .37, p < .00), and health responsibility (r = .31, p = .001) and negatively related to illness self-blame (r = -.35, p < .001). Immediately after receiving risk information, SC was inversely related to negative affect (r = -.23, p = .01) controlling for self-esteem. SC predicted unique variance, over self-esteem, with seeking health information (B = -.87, r2 = .08, p = .05) over the following week. SC may assist with adaptive reactions and self-regulation after learning one is at-risk for CVD.Acknowledgments: University of Manitoba Graduate Fellowship
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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 | 0.009 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".