An International Perspective on Priority Areas for Women’s Cardiovascular Health Research: Qualitative Findings From a Cross-Sectional Survey
Bibliographic record
Abstract
Background: The Global Cardiovascular Research Funders Forum (GCRFF) unites international research funders interested in supporting cardiovascular disease (CVD) research. One focus area is women's cardiovascular health; the present study aimed to identify priority themes for future joint research funding opportunities. Methods: After ethics approval, a survey was developed based on literature review, GCRFF feedback, and input from methodologists/content experts. The survey comprised 3 open-ended and 5 multiple-choice questions. With the use of widespread dissemination strategies, an electronic survey portal was opened for 8 weeks, from November 15, 2023, to January 15, 2024, with various language options. Results were downloaded into a secure REDCap database for analysis. Independent theming of responses was completed by 3 reviewers until coding consensus was achieved, following which 1 coder completed the remaining theming. Descriptive statistics are reported. Results: Among the 191 responses, all 9 GCRFF countries were represented. Most respondents identified as women (74%) and clinicians/academics (77%); fewer people with lived or living experience participated (23%). Common themes included women-specific risk factors and prevention strategies (56%), life-course issues (43%), and sex- and gender-specific treatments and outcomes (36%). Common topics were CVD (66%), coronary artery disease (CAD) (18%), and heart disease (13%). Designated research pillars included clinical (52%), population health (36%), and basic science (30%). Solutions proposed included knowledge generation (71%), increased funding (55%), networking researchers (52%), and knowledge mobilisation (41%). Congruence of priority ordering was demonstrated between sex and respondent residence subgroups. Conclusion: Diverse international input prioritised research in risk factors and prevention strategies specific to women and in sex- and gender-specific treatment and outcomes of heart disease, with considerations of life-course issues across all research pillars.
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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 | 0.048 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".