Burnout among postgraduate medical trainees in Lebanon: Potential strategies to promote wellbeing
Bibliographic record
Abstract
Objective: Burnout is a widespread issue in healthcare for many years. Lebanon combatted political and economic crises before the coronavirus disease 2019 (COVID-19) pandemic, in addition to the port explosion in August 2020. The study aimed to identify the determinants of personal burnout, patient-related burnout, and work-related burnout among postgraduate medical trainees (PGMT) and evaluate its relationship with sociodemographic characteristics. Design: A cross-sectional study utilized the Copenhagen Burnout Inventory (CBI) involving electronic, voluntary, and anonymous survey. The survey was completed by 188 PGMT including residents and fellows from all specialties and all levels of training. Results: The prevalence rates are 68.6% for personal burnout, 63.3% for work-related burnout, and 35.1% for patient-related burnout. Conclusion: Results improve our understanding of the phenomenon of burnout, and the role of program leadership in shaping the impact of burnout on training and promoting wellbeing of PGMT. Discussion focuses on providing potential wellbeing strategies for program directors to follow for mitigating burnout.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".