‘Is this worth getting into a big fuss over?’ Everyday racism in medical school
Bibliographic record
Abstract
INTRODUCTION: Faced with an increasingly diverse student body, educators in the health professions struggle for ways to foster equality and understand racism. The concept of 'everyday racism' provides an important tool for examining subtle processes that construct a racialised climate in medical schools and other institutions. OBJECTIVES: To examine the ways racism is understood and experienced within one medical school and investigate the micro level interactional processes that may perpetuate inequality. METHODS: A survey (n = 72) and interviews (n = 25) were conducted with third year students at one Canadian medical school. A second class was surveyed (n = 61) 3 years later and 25 more students were interviewed. RESULTS: Students identified the linguistic advantage enjoyed by some classmates from ethno-cultural minority groups, but were less likely to identify the advantages enjoyed by white students, who may be more readily granted student-doctor status. Students from racialised minority groups experienced marginalisation through segregation, and struggled to respond appropriately to racist jokes and comments from patients and staff. A third (29%) of those who identified as 'minority' group members did not feel they fitted in particularly well at medical school, compared with only 7% of 'non-minority' students (chi2 P = 0.006; t-test P = 0.004). CONCLUSION: Medical students from racialised minority groups may experience 'everyday racism', mundane daily practices which intentionally or unintentionally convey disregard, disrespect or marginality. Such experiences are particularly difficult to deal with. Educators have a responsibility to counter with sustained antiracism, learning to acknowledge salient differences without reinforcing hierarchies of superiority and inferiority.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".