Stress Management and Resilience Intervention in a Women's Heart Clinic: A Pilot Study
Bibliographic record
Abstract
Background: In general, women report higher stress levels than men. High baseline anxiety, depression, and stress levels are associated with greater risk of cardiovascular diseases. Current evidence for efficacy of stress management interventions for women is limited. This study aimed at assessing the effect of a stress management and resiliency training (SMART) program for decreasing stress, anxiety, and depressive symptoms. Methods: Fifty moderately or severely stressed Women's Heart/Preventive Cardiology Clinic patients consented to the SMART intervention delivered online ( n = 36) or in-person ( n = 9). Primary outcome measures were the observed changes between baseline and at 12 weeks for the following psychometric tools: General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaires (PHQ-9), Perceived Stress Scale (PSS), and Brief Resiliency Scale (BRS). Results: Forty-five patients completed the study. We observed significant improvements in PSS and GAD-7, but not in PHQ-9 or BRS, after the SMART intervention. When assessing outcomes among those with depressive symptoms at baseline (PHQ-9 > 15), we observed significant changes in PSS, GAD-7, and PHQ-9. No differences between online and in-person program delivery methods were found (all p -values >0.05). Conclusions: Training exposure using the SMART program to decrease stress and anxiety in women seeking preventive cardiology services was feasible and similarly effective, whether delivered online or in a single in-person session. Impacts on depression and resilience likely require a more intensive approach. In the future, larger randomized clinical trials with additional training and longer follow-up are warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".