Self-Management of Cardiac Pain in Women: A Meta-Summary of the Qualitative Literature
Bibliographic record
Abstract
Symptom recognition and self-management is instrumental in reducing the number of deaths related to coronary artery disease (CAD) in women. The purpose of this study was to synthesize qualitative research evidence on the self-management of cardiac pain and associated symptoms in women. Seven databases were systematically searched, and the concepts of the Individual and Family Self-Management Theory were used as the framework for data extraction and analysis. Search strategies yielded 22,402 citations, from which 35 qualitative studies were included in a final meta-summary, comprising data from 769 participants, including 437 (57%) women. The available literature focused cardiac pain self-management from a binary sex and gender perspective. Ethnicity was indicated in 19 (54%) studies. Results support individualized intervention strategies that promote goal setting and action planning, management of physical and emotional responses, and social facilitation provided through social support.
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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 | 0.051 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".