Abstract 15642: Discrimination Against Physicians in Interventional Cardiology
Bibliographic record
Abstract
Background: Data on discrimination against physicians in the field of interventional cardiology are limited. Methods: We performed an online, anonymous, international survey of interventional cardiologists. Results: A total of 445 interventional cardiologists participated in the survey. Median age was 46-50 years and most (61%) practice in the United States. Among the participants, 13% were women and 13% belonged to underrepresented minority groups. Participants were asked to discern which characteristics contribute to discrimination against physicians in their workplace. Several characteristics were identified (Figure): language accent (48%), race/ethnicity (44%), age (44%), gender (43%), and appearance/clothing (40%). Women were more likely to have experienced discrimination by patients and families (81% vs 54%; p<0.001), peers (81% vs 45%; p<0.001), supervisors (73% vs 43%; p<0.001), support staff (64% vs 36%; p<0.001), and nursing staff (63% vs 37%; p=0.001), compared with men. Similarly, among participants who practice in the US, UK, Canada, and Australia (n=296), physicians whose first language was not English were more likely to have experienced discrimination from patients and families (73% vs 46%; p<0.001), peers (63% vs 35%; p<0.001), supervisors (56% vs 33%; p<0.001), support staff (51% vs 28%; p<0. 001), and nursing staff (52% vs 31%; p=0.001), compared with native English speakers. Both women (60% vs 7%; p<0.001) and underrepresented minority groups (28% vs 12%; p=0.003) were more likely to be mistaken for a non-physician employee, compared with men and non-underrepresented minority groups respectively. While 9% (n=42) of the total participants reported incidents of discrimination to their respective organizations, only 19% of those (n=8) expressed satisfaction with the response received. Conclusions: Our survey provides a snapshot of the current status of discrimination faced by interventional cardiologists.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".