Redefining global cardiac surgery through an intersectionality lens
Bibliographic record
Abstract
Although cardiovascular diseases are the leading cause of morbidity and mortality worldwide, six billion people lack access to safe, timely and affordable cardiac surgical care when needed. The burden of cardiovascular disease and disparities in access to care vary widely based on sociodemographic characteristics, including but not limited to geography, sex, gender, race, ethnicity, indigeneity, socioeconomic status and age. To date, the majority of cardiovascular, global health and global surgical research has lacked intersectionality lenses and methodologies to better understand access to care at the intersection of multiple identities and traditions. As such, global (cardiac) surgical definitions and health system interventions have been rooted in reductionism, focusing, at most, on singular sociodemographic characteristics. In this article, we evaluate barriers in global access to cardiac surgery based on existing intersectionality themes and literature. We further examine intersectionality methodologies to study access to cardiovascular care and cardiac surgery and seek to redefine the definition of 'global cardiac surgery' through an intersectionality lens.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".