Assessing Cultural Sensitivity Questions in Ranking Decisions for a Family Medicine Residency Program
Bibliographic record
Abstract
Background and aims/objectives:The ability of family physicians to establish an inclusive and culturally-safe practice environment is a key competency of the profession.Our study aimed to explore the utility of questions assessing cultural sensitivity to improve ranking decisions of family medicine residency candidates.Methods: A series of cross-sectional online surveys were sent to interviewers (current residents and faculty), site directors and administrators, following completion of the first period of national resident interview dates.The surveys contained both closed and open-ended questions about the utility of cultural sensitivity questions during the interview process.Frequency distributions were calculated in Microsoft Excel for the 5-point Likert items.Open-ended data was themed by an independent researcher.This project was exempted by the Behavioural Research Ethics Board of the University of Saskatchewan.Results: The majority of respondents felt the questions helped them identify candidates that would fit the program.Local modifications were done to adapt to local context or improve clarity.For example, questions were generalized to vulnerable populations or narrowed specifically to experiences with Indigenous populations.Some participants indicated that cultural knowledge, as opposed to empathy, can be taught and thus the latter is what the assessment of candidates' abilities should focus on.It was also suggested that these questions detract from opportunities to assess "particularly relevant clinical experiences or personal experiences outside of medicine."Conclusions and Recommendation: Interviewers generally felt cultural sensitivity questions improved ranking decisions.Additionally, allowing flexibility to adapt to local contexts was important.Future initiatives can focus interviewer training on cultural sensitivity/safety approaches.
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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 | 0.114 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".