A survey-based study about burnout among postgraduate medical trainees: implications for leaders in healthcare management
Bibliographic record
Abstract
Introduction The goal of the study is to assess burnout among postgraduate medical trainees, evaluate the association with sociodemographic features and offer potential wellness strategies for leaders responsible for their education, training, management, and wellbeing. Methods The Oldenburg Burnout Inventory was used. The web-based, voluntary, and anonymous survey was sent to postgraduate medical trainees from various specialties and all years of training in a tertiary medical center in Beirut, Lebanon. Additional questions were added after the survey regarding reporting channels for burnout and possible interventions for wellbeing. Results The total number of valid responses are 188. The prevalence rates of high burnout are 37.2% for disengagement and 51.1% for exhaustion. There is a significant difference between the mean of exhaustion and gender (p = 0.003). There is a significant difference between the mean of disengagement and year of training (p = 0.017). There is a significant difference between the mean of exhaustion and year of training (p = 0.029). There is a significant difference between the frequency of disengagement and year of training (p = 0.027). Conclusion The study reveals how postgraduate medical training program is impacted by the existing challenges from social, health, and financial standpoint, along with the instabilities encountered such as multiple wars and port blast in 2020 and how these variables aggravate burnout. Burnout severely impacts the education and training of PGMT and promoting wellbeing can help reverse the process. Findings contribute to establishing effective strategic interventions for leaders in healthcare management to adopt.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".