Burnout among the Clinical Dental Students in the Jordanian Universities
Bibliographic record
Abstract
BACKGROUND: The study aimed to evaluate the level of burnout among the clinical dental students in two Jordanian universities. METHODS: A total of 307 students from the two schools were surveyed using Maslach Burnout Inventory survey. Scores for the inventory's subscales were calculated and the mean values for the students' groups were computed separately. Kruskal-Wallis and Mann-Whitney tests were carried out and the results were compared at 95% confidence level. RESULTS: The results showed that the dental students in both Jordanian universities suffered high levels of emotional exhaustion and depersonalization compared to reported levels for dental students in other countries. The dental students of the University of Jordan demonstrated a significantly higher (p < 0.05) level of emotional exhaustion than their counterparts in the Jordan University of Science and Technology. CONCLUSIONS: The findings indicated that dental students in the Jordanian universities presented considerable degrees of burnout manifested by high levels of emotional exhaustion and depersonalization. Studies targeting students health and psychology should be carried out to determine the causes of burnout among dental students. The curricula of the dental schools in the two universities should be accordingly improved to minimize burnout among the students. KEYWORDS: Burnout; Emotional exhaustion; Depersonalization; Personal accomplishment; Maslach Burnout Inventory.
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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 | 0.002 |
| 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) | 0.002 | 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".