Mass media narratives of women’s cardiovascular disease: a qualitative meta-synthesis
Bibliographic record
Abstract
Cardiovascular disease is the leading cause of death and disability among women worldwide. Narratives circulated by the media regarding women's identities and health constitute one source of meanings by which conceptualisations about risk, risk reduction, and disease prevention are formed and framed. An interpretive and integrative meta-synthesis of qualitative research was done to examine the representations of women's cardiovascular disease in traditional and user-generated Canadian and US media narratives, and explore the implications of these for gendered identities and health promotion for women. After a literature search of electronic databases, 29 qualitative peer-reviewed journal articles published since 2000 met the eligibility criteria and were included for review. The findings revealed three overarching themes: (a) the construction of who is at risk for cardiovascular disease; (b) the portrayal of certain risk-reducing strategies and acute events; and (c) the delegation of responsibility for maintaining female cardiovascular health. These meta-synthesis findings contribute towards novel understandings about the culture of women's cardiovascular disease risk and the feminisation of healthism/individual responsibility, which may limit awareness among marginalised female demographics (those from lower socio-economic and minority racial backgrounds).
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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 | 0.030 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".