Workplace discrimination and harassment among Alberta postgraduate medical trainees: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Addressing a lack of diversity in the physician workforce is a priority in the Canadian healthcare system. Data describing demographics of residents and their experiences of discrimination, harassment, and racism at work are incomplete. The objective of this work was to describe the demographics and perceptions of workplace discrimination and harassment for postgraduate medical trainees in Alberta. METHODS: A cross-sectional survey based on the Chronic Workplace Discrimination and Harassment Scale was administered to all resident physicians in Alberta by e-mail invitation from the postgraduate medical education offices at the Universities of Alberta and Calgary, their residency training program directors, and the Professional Association of Residents of Alberta. Total score (median, interquartile range [IQR]) was compared by gender, racial, and intersecting gender and racial identities, with higher scores suggesting more frequent experiences of workplace discrimination and harassment (range 0 to 32). We performed thematic content analysis of open text responses. RESULTS: There were 195 complete surveys returned from 1,752 Alberta residents (11.2% response rate), including 120 cisgender women (61.5%), 104 white participants (53.3%) and 74 white cisgender women (37.9%). The overall median score on the Chronic Workplace Discrimination and Harassment scale was 9 (IQR 5-14): cisgender women and gender diverse participants reported more frequent harassment, mistreatment, or discrimination than cisgender men (median 10 [IQR 6-15] versus 8 [4-13.5], p = 0.049). There was no difference in the frequency of reported discrimination between BIPOC and white respondents (median 9 [IQR 5-14.5] versus 9.5 [IQR 6-14], p = 0.72) or participants with intersecting race and gender identities (p = 0.26). Over 44% of BIPOC residents had been the target of a racial slur or joke from an attending physician or colleague in the past year and nearly 45% of all participants had witnessed an attending physician or colleague using a racial slur in the past year. Open text responses provided examples of mistreatment, harassment, discrimination, and racism from participants. INTERPRETATION: These results demonstrate an unacceptable prevalence of harassment and racism witnessed or experienced at work by Alberta residents. Urgent action to identify, prevent, and remediate racism in the healthcare system must be a priority of medical schools and regulatory bodies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".