‘In a perfect world doctors and the medical profession would accept people for who they are’: women’s heart health information practices
Bibliographic record
Abstract
Introduction. This exploratory study investigates women’s health information practices by examining how women perceive and interpret heart health information from organizations such as Heart & Stroke Canada that are targeted specifically to them. Method. Focus groups were conducted with women (45 – 90 years) with heart disease and without heart disease and three women with heart disease participated in semi-structured interviews. Analysis. The data were analysed using thematic analysis. Five themes that shaped women’s perceptions and interpretations of heart health information were identified: personal expertise and experiential knowledge, consistency in information content, embodied information, ability to act on information, and shame and blame. Results. Women draw from epistemic, social, and corporeal information sources in order to make sense of heart disease. Coupling corporeal and experiential knowledge are important for women to triangulate information. Women’s heart health information practices occur within an androcentric, sociocultural context where broader social information sources that focus on ‘ideal’ health standards must align with women’s lived experiences in order for the information to be acted upon. Conclusions.To craft more effective messages and provide helpful information about heart health, messages and information must align with women’s information practices in ways that acknowledge the intersections and consistency of epistemic, social, and corporeal information sources and the information must be actionable.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".