Systematic review of academic bullying in medical settings: dynamics and consequences
Bibliographic record
Abstract
PURPOSE: To characterise the dynamics and consequences of bullying in academic medical settings, report factors that promote academic bullying and describe potential interventions. DESIGN: Systematic review. DATA SOURCES: We searched EMBASE and PsycINFO for articles published between 1 January 1999 and 7 February 2021. STUDY SELECTION: We included studies conducted in academic medical settings in which victims were consultants or trainees. Studies had to describe bullying behaviours; the perpetrators or victims; barriers or facilitators; impact or interventions. Data were assessed independently by two reviewers. RESULTS: We included 68 studies representing 82 349 respondents. Studies described academic bullying as the abuse of authority that impeded the education or career of the victim through punishing behaviours that included overwork, destabilisation and isolation in academic settings. Among 35 779 individuals who responded about bullying patterns in 28 studies, the most commonly described (38.2% respondents) was overwork. Among 24 894 individuals in 33 studies who reported the impact, the most common was psychological distress (39.1% respondents). Consultants were the most common bullies identified (53.6% of 15 868 respondents in 31 studies). Among demographic groups, men were identified as the most common perpetrators (67.2% of 4722 respondents in 5 studies) and women the most common victims (56.2% of 15 246 respondents in 27 studies). Only a minority of victims (28.9% of 9410 victims in 25 studies) reported the bullying, and most (57.5%) did not perceive a positive outcome. Facilitators of bullying included lack of enforcement of institutional policies (reported in 13 studies), hierarchical power structures (7 studies) and normalisation of bullying (10 studies). Studies testing the effectiveness of anti-bullying interventions had a high risk of bias. CONCLUSIONS: Academic bullying commonly involved overwork, had a negative impact on well-being and was not typically reported. Perpetrators were most commonly consultants and men across career stages, and victims were commonly women. Methodologically robust trials of anti-bullying interventions are needed. LIMITATIONS: Most studies (40 of 68) had at least a moderate risk of bias. All interventions were tested in uncontrolled before-after studies.
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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 | 0.022 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".