Patterns of Ostracism Experienced by Canadian Medical Trainees of Asian Sub-ethnicities
Bibliographic record
Abstract
Phenomenon: Ostracism has negative effects on one’s fundamental needs. North Americans of Asian ethnicities are at an increased risk of ostracism due to stereotypes labeling them as inherently different to Western cultural norms. We explored Asian Canadian medical trainees’ experiences with ostracism during their clinical training. Approach: We conducted semi-structured interviews with 20 medical trainees of Asian ethnicities at 3 Canadian medical schools to explore experiences of ostracism and conducted a thematic analysis guided by the theoretical framework of the temporal need threat model of ostracism. Findings: Participants from East-, South-, and Southeast-Asian sub-ethnic groups completed the study. They voiced experiences of being excluded from clinical and social settings. Ostracism was mainly fueled by systemic racism, power dynamics in medical education, and non-diverse training environments. The model minority myth was a significant contributor to experiences of ostracism. Trainees felt their well-being threatened and many felt resigned to accept ostracism going forward. Insights: Ostracism poses a significant threat to the wellbeing and career progression of Asian Canadian medical trainees. Trainees facing covert ostracism were particularly at risk of entering the resignation stage of hopelessness. This underrecognized problem needs to be addressed by institutions to dismantle harmful stereotypes and prejudiced practices facing these minoritized communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".