A qualitative study of academic bullying among junior doctors in Sierra Leone
Bibliographic record
Abstract
INTRODUCTION: Academic bullying of junior doctors undermines trainee wellbeing and patient care, yet little is known about its manifestation in Sierra Leone. This qualitative study explored the experiences, perceptions, and coping strategies of junior doctors subjected to academic bullying at the University of Sierra Leone Teaching Hospitals Complex (USLTHC). METHODS: Guided by a social-constructivist paradigm, we conducted semi-structured interviews with 29 junior doctors across major hospitals that comprise the USLTHC. Data were collected between July 20 and August 31, 2024, using an English-language interview guide. Thematic analysis was undertaken with NVivo software. RESULTS: Eight inter-related themes emerged: (1) entrenched clinical hierarchies, (2) overt verbal abuse and humiliation, (3) punitive workloads and extended shifts, (4) lack of institutional safeguards, (5) psychological distress, (6) threatened career progression and attrition, (7) reliance on peer-support coping, and (8) proposed remedies, notably teacher-training for seniors and enforceable anti-bullying policy. Bullying was framed as a cyclical "rite of passage" perpetuated by fear of retaliation and scarce reporting channels. CONCLUSION: Academic bullying at USLTHC is systemic, culturally rationalised, and exacerbated by resource constraints. Embedding enforceable anti-bullying policies, structured faculty-development, and accessible mental-health services is essential to protect trainees and protect Sierra Leone's evolving health-workforce.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| 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".