Stress in Medical Students in a Problem-Based Learning Curriculum
Bibliographic record
Abstract
Background This study aims to assess stress level and its drivers among medical students using a PBL teaching system Method Higher Education Stress Inventory (HESI,) was used to assess stress among medical students. . All students in the College of Medicine were enrolled. Results: The response rate was 99%.The prevalence of stress was 54.7%. The overall mean stress score was higher in the 4 th year students (2.64) than 1 st year students (2.52) (p= 0.01). Junior students were more likely to be stressed by lack of clarity of the aims of the study (p=0.014) and lack of feedback from the teachers (p=0.003). Senior students were more likely to be stressed by lack of time for other activities (p=0.036), financial worries (p=0.027)) and about preparedness for future profession (p=0.007) Despite the high stress scores, only 8.3% regretted their choice of career and 9.3 % felt that they are not prepared well for their future profession Conclusions High level of stress was noted especially among senior students. Stress in junior students was more likely to be medical training-related and to be personal problems-related in senior students. The vast majority of students were happy with their choice of profession and optimistic about their future
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".