Health professions school applicant experiences of discrimination during interviews
Bibliographic record
Abstract
Bias pervades every aspect of healthcare including admissions, perpetuating the lack of diversity in the healthcare workforce. Admissions interviews may be a time when applicants to health profession education programs experience discrimination. Between January and June 2021 we invited US and Canadian applicants to health profession education programs to complete a survey including the Everyday Discrimination Scale, adapted to ascertain experiences of discrimination during admissions interviews. We used chi-square tests and multivariable logistic regression to determine associations between identity factors and positive responses. Of 1115 respondents, 281 (25.2%) reported discrimination in the interview process. Individuals with lower socioeconomic status (OR: 1.78, 95% CI [1.26, 2.52], p = 0.001) and non-native English speakers (OR: 1.76, 95% CI [1.08, 2.87], p = 0.02) were significantly more likely to experience discrimination. Half of those experiencing discrimination (139, or 49.6%) did nothing in response, though 44 (15.7%) reported the incident anonymously and 10 (3.6%) reported directly to the institution where it happened. Reports of discrimination are common among HPE applicants. Reforms at the interviewer- (e.g. avoiding questions about family planning) and institution-level (e.g. presenting institutional efforts to promote health equity) are needed to decrease the incidence and mitigate the impact of such events.
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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 | 0.040 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".