First year medical student stress and coping in a problem‐based learning medical curriculum
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of psychological morbidity, sources of stress and coping mechanisms in first year students in a problem-based learning undergraduate medical curriculum. DESIGN: Longitudinal cohort questionnaire survey. SETTING: Glasgow University Medical School. PARTICIPANTS: All first year students (n = 275) in the 1997-98 intake. MAIN OUTCOME MEASURES: Scores on the 12-item General Health Questionnaire (GHQ-12), sources of stress and coping strategies. RESULTS: The prevalence of psychological morbidity and mean GHQ-12 scores increased significantly between term 1 and term 3, with no significant gender differences. Principal stressors were related to medical training rather than to personal problems, in particular uncertainty about individual study behaviour, progress and aptitude, with specific concerns about assessment and the availability of learning materials. The group learning environment, including tutor performance, and interactions with peers and patients caused little stress. Students generally used active coping strategies. Both stressor group scoring and coping strategies showed some variation with gender and GHQ caseness. CONCLUSIONS: Increased student feedback and guidance about progress throughout the year and the provision of adequate learning resources may reduce student stress. Educational or pastoral intervention regarding effective coping strategies may also be beneficial. Continued follow-up of this cohort could provide information to inform further curriculum development and, if appropriate, aid the design of programmes for the prevention of stress-related problems.
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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 | 0.004 |
| 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 | 0.000 |
| 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".