Tackling Workplace Bullying for Minority Ethnic Doctors
Bibliographic record
Abstract
Workplace bullying, undermining and microaggressions are a reality for many, and although the prevalence may vary, there is no environment that is free of such hostile interactions. The healthcare workforce is focussed on empathy, kindness and caring, yet the daily experiences of many are in stark contrast to this. Although awareness of these issues exist, incidents of bullying are still grossly under-reported. Bullying and undermining behaviours stem from a gradient of power and lack of appreciation of the societal advantages of diversity. In keeping with this, the experience of particular sub-populations are disproportionately worse, such as for women, minority ethnic groups, those with disability, LGBTQ+ and those from deprived backgrounds. There have been campaigns and initiatives to change workplace behaviours, with mixed successes. A less explored role is that of organisations whose declared mission is to stand up for equality, represent the voice of the minorities and the under-represented, akin to self-help groups and advocacy. This article explores workplace bullying from the perspective of the minority ethnic doctors and proposes the potential benefit of their representative organisations in helping to balance the inherent workplace disadvantages.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".