Burnout, Stress, and Stimulant Abuse among Medical and Dental Students in the Western Region of Saudi Arabia
Bibliographic record
Abstract
Background: High levels of burnout, stress, and stimulant abuse have been reported among medical and dental students worldwide, with country-specific factors being contributors. The association, risk factors, and predictors of these three variables have not sufficiently been reported from Saudi Arabia, especially from the Western region. Objective: To determine the prevalence, association, and predictors of burnout, stress, and stimulant abuse among medical and dental students in the Western region of Saudi Arabia. Methods: This cross-sectional study included all second to sixth year medical and dental students enrolled at Taibah University, Madinah, Saudi Arabia, during the 2019-2020 academic year. A self-administered, closed online questionnaire was administered. Data regarding stress were elicited using Cohen's 10-item Self-Perceived Stress Scale and regarding burnout using the Oldenburg Burnout Inventory Student Version questionnaire. Multiple logistic regression model to identify the risk of burnout was conducted, and univariate and multiple linear regression models were carried out to identify the predictors of stress. Results: Of 1016 eligible students, 732 responded (medical: 511; dental: 221). About half of the students experienced burnout (51.5%), with both high disengagement (49%) and exhaustion (45%). Most participants (90.3%) experienced moderate levels of stress. Eight (1.1%) respondents had experienced stimulant abuse; there was a no significant association between stimulant abuse and burnout in the multivariate analysis. Stress, age, gender, body mass index, GPA, study field, smoking, family income, and birth order were significant predictors of burnout, while burnout, age, gender, GPA, and physical exercise were significant predictors of stress. Conclusion: The findings in this study highlight the need for policymakers to devise strategies that target early identification as well as reduction of the high levels of burnout and stress.
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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 | 0.001 | 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".