Physical fitness of medical residents: Is the health of surgical residents at risk?
Bibliographic record
Abstract
Background: Postgraduate medical residency programs are laborious and time-intensive, and can be physically, intellectually and emotionally demanding. These working conditions may lead to the neglect of personal health and well-being. The objective of this study was to compare the anthropometric and fitness characteristics of surgical and nonsurgical medical residents. We hypothesized that there is a difference in physical health between these 2 groups. Methods: Medical residents completed a demographic information questionnaire and were assessed by trained staff for height, weight, body fat percentage, muscular strength and endurance, and peak oxygen consumption (V̇o2peak). The average number of working hours per week was also documented. Results: Forty-five residents (21 surgical and 24 nonsurgical; 31 men and 14 women) participated in the study. Surgical residents worked more hours per week on average than nonsurgical residents (p = 0.02) and had a higher body mass index (BMI) (p = 0.04) and lower V̇o2peak (p = 0.01). Conclusions Surgical residents worked more hours than nonsurgical residents, which may have contributed to their higher BMI and lower aerobic fitness levels. Despite a heavy workload, it is important for all medical residents to find strategies to promote a healthy lifestyle for both themselves and their patients to ensure long-term well-being.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".