Prevalence of Burnout among 7th Year Lebanese University Medical Students and Its Association with Anxiety, Depression, and Other Factors
Bibliographic record
Abstract
Background & Objectives: Burnout Syndrome and its three interrelated dimensions: emotional exhaustion, depersonalization, and personal accomplishment poses an enormous burden on a person’s familial and social life. This syndrome is problematic as it affects individuals differently and may lead to dropping off from work, work-related errors, and even suicidal ideation. Burnout in medical students is a relatively new topic and no previous studies were performed in Lebanon on this population of interest along with its association with anxiety and depression. The primary aim of this study is to determine the prevalence of burnout among Lebanese University 7th-year medical students rotating in private and public national hospitals and to elucidate any association with depression, anxiety, and several other factors. Subjects & Methods: A total of 137 Lebanese University 7th-year medical students rotating from July 2019- June 2020 in different national hospitals were surveyed using the Maslach Burnout Inventory for Health Services Workers (MBS-HSS), The Hamilton Anxiety Rating scale (HAM-A), and the Hamilton Depression Rating Scale (HAM-D or HDRS). Data analysis was conducted using IBM SPSS version 21. The relationship between the variables was studied using chi-square testing, and the p-value (<0.05) was considered statistically significant. Results: A total of 112 students responded to the questionnaire (response rate of 81.75%). The prevalence of burnout was mainly high in low Personal Accomplishment (67%), followed by Emotional Exhaustion (37.5%), with the lowest prevalence rate for depersonalization (25.9%). 84.8% of the students had high burnout in at least one domain. The prevalence of anxiety and depression was found to be 7.2% and 41.9%, respectively. The Number of calls and prescription errors was found to be strongly correlated with burnout level. Furthermore, anxiety and depression were found to be associated with high burnout levels. Conclusion: This study sheds the light on the high burnout level experienced by medical students at Lebanese University and the necessity to conduct more studies to investigate the causes and to develop different coping strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".