Burnout Syndrome in Medical Students in the Kingdom of Bahrain
Bibliographic record
Abstract
OBJECTIVES: To assess stress and burnout, and identify common stressors, among medical students in the Kingdom of Bahrain. STUDY DESIGN: A cross-sectional study with students being evaluated from March to September 2017 at two medical colleges in the Kingdom of Bahrain. METHODOLOGY: Survey conducted on a total sample of 533 clerkship-training students with a total of 347 respondents. The instruments used were Cohen’s Perceived Stress Scale; the Maslach Burnout Inventory; and a common stressors questionnaire. RESULTS: 65% (347/533) of the students from the two medical colleges responded to the questionnaire. It was found that the mean (SD) of Cohen stress score in this study was 21.76 (5.60), with a stress and burnout prevalence of 47% and 43.43% respectively. A high percentage of respondent students (68%) also exhibited high emotional exhaustion scores > 14. More than half of the respondents (53.3%) exhibited high cynicism score > 6. Statistically significant differences were observed across gender categories with Cohen mean score, emotional exhaustion and cynicism. Multiple linear regressions revealed gender to be the only statistically significant predictor of the Cohen score (p. value 0.042). CONCLUSION: Clerkship medical students displayed high levels of both stress and burnout prevalence. Medical educators must be aware of the early signs, causes and consequences of student stress. They should also be able to encourage students to improve their mental and physical health, promote mental well-being and teach stress management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".