‘Flying below the radar’: a qualitative study of minority experience and management of discrimination in academic medicine
Bibliographic record
Abstract
OBJECTIVES: This paper aims to give voice to the lived experience of faculty members who have encountered racial or ethnic discrimination in the course of their academic careers. It looks at how they describe the environment for minorities, how they manage discrimination and what institutions and majority-member faculty can do to improve medical academe for minority members. METHODS: Qualitative techniques were used for semi-structured, in-depth individual telephone interviews, which were audiotaped, transcribed and analysed by reviewers. Themes expressed by multiple faculty members were studied for patterns of connection and grouped into broader categories. A description of the faculty sample is provided, in which respondents ranked the importance of discrimination in hindering academic advancement and used Likert scales to evaluate effects of discrimination. The sample was drawn from 12 of 24 academic medical centres in the National Faculty Survey and included 18 minority-member faculty staff stratified by gender, rank and degree who had experienced, or possibly experienced, work-related discrimination. RESULTS: Minority faculty described the need to be strongly self-reliant, repeatedly prove themselves, develop strong supports and acquire a wide range of academic skills to succeed. Suggested responses to discrimination were to be cautious, level-headed and informed. Confronting discriminatory actions by sitting down with colleagues and raising the level of awareness were important methods of dealing with such situations. CONCLUSIONS: Academic medical centres may need to make greater efforts to support minority faculty and improve understanding of the challenges confronting such faculty in order to prevent the loss and/or under-utilisation of important talent.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".