A Sociocultural Exploration of Sex-Bias in NSERC-funded Human Cardiovascular Research at Ontario Universities
Bibliographic record
Abstract
It is well established that sex-bias against the inclusion of women exists in human cardiovascular research, where women have been excluded from or under-represented in the research process, despite the high prevalence of cardiovascular disease among this population. To address the sex-bias against the inclusion of women in federally-funded research, including research funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (DG) program, the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans was introduced in 2010. However, despite the introduction of this policy, it remains unknown whether a sex-bias persists in NSERC DG-funded basic human cardiovascular research at Ontario universities. The purpose of this research is twofold. (1) Using a quantitative analysis, this research will determine the presence or absence of a sex-bias against the inclusion of women in NSERC DG-funded basic human cardiovascular research at Ontario universities from policy implementation to the present. After analysis of all NSERC DG-funded publications (n=96), female exclusion or under-representation was evident in 63% of publications. (2) By conducting semi-structured interviews with Ontario university basic human cardiovascular researchers (n=5) and by using thematic analyses, this study will characterize the sex-bias against the inclusion of women, and by using a sociocultural lens, will explore how research, as a social institution, may act to construct, maintain and reinforce sex inequalities. This research will highlight practical changes that could occur to challenge this sex-bias and increase female inclusion in cardiovascular research, ultimately with the goal of improving female cardiovascular health.
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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 | 0.073 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| 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".