A survey of undergraduate medical students’ knowledge and attitudes about gender bias: the Newfoundland and Labrador perspective
Bibliographic record
Abstract
Background: Female physicians and patients experience gender bias in healthcare. The purpose of this research is to explore medical students’ gender bias toward physicians and patients and whether their bias varies by gender. Methods: We surveyed medical students at Memorial University between November 2020 and April 2021. We recruited participants through Facebook, email, and e-posters. We collected demographic information, including gender and class year. We used the Nijmegen Gender Awareness in Medicine Scale to measure gender sensitivity, gender role ideology toward patients, and gender role ideology toward doctors. We analyzed the data using averages and t-tests. Results: Mean gender sensitivity scores were 4/5 indicating high gender sensitivity. Gender role ideology toward doctors mean scores were 2/5 indicating that students did not hold strong stereotypical views toward doctors. Although male students scored higher than female students (p<.05), mean scores for gender role ideology toward patients were low for both male and female students (x̄<2), indicating low stereotyping toward patients. Conclusions: We found that students held largely non-biased ideologies surrounding gender in medicine and that female students were even less biased than male students for gender role ideology toward patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".