The influence of time-management on medical resident's perceived stress scale and overtime: A tertiary care hospital experience from Switzerland
Bibliographic record
Abstract
Background and Aim: Many physicians express challenges in managing time demands during clinical practice. Effective time-management skills may counter the negative effects of high workload and time pressures, and subsequently improve productivity as well as professional and personal wellness. The objective is to determine the impact of time-management training on medical residents' overtime hours and self-reported stress scores. Materials and Methods: The present study employed a within-subjects, single group pretest-posttest design. Convenience sampling was used to recruit 27 medical residents from a tertiary care hospital in Saint Gall, Switzerland. Each resident participated in a single, 2-h interactive time-management workshop. Each resident completed the Perceived Stress Scale-10 (PSS-10) 1-week before and 8-week after the time-management intervention. Overtime of every resident was documented during the same time intervals using a hospital-based computer system. Finally, we recorded whether residents experienced any stressful life events during this 8-week study. Results: Residents worked significantly fewer overtime hours after the time-management intervention than before the intervention (P = 0.01). There was also a significant reduction in mean PSS scores following the intervention, but only for residents who reported experiencing stressful life events during the study (P = 0.028). The intervention had no effect on mean PSS scores for residents who did not report a stressful life event (P = 0.36). Conclusion: The present study reported an effect of time-management training on overtime and perceived stress in residents. We argue that time-management can help residents cope with external stressors, and that individuals who experience stressful life events may especially benefit from time-management training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".