Physical Activity Levels and Counseling Practices of U.S. Medical Students
Bibliographic record
Abstract
INTRODUCTION: Some literature shows a positive relationship between physicians' personal physical activity (PA) levels and physicians' encouraging patients' PA, though it remains unclear how this evolves during medical training. In this paper, we describe U.S. medical students' PA levels and factors predicting relevance and frequency of their PA counseling of patients. METHODS: This is a prospective survey of a representative sample of U.S. medical students from 16 schools (N = 2316) designed to determine health-related attitudes and practices. Student's PA levels were assessed using a Godin exercise questionnaire. An 80.3% (N = 1658) response rate was achieved during 4 yr. RESULTS: More than half (61%) of U.S. medical students adhered to CDC PA recommendations. This rate was relatively stable during the 4 yr of medical training. Of those who reported a lot of stress in the last 12 months or 2 wk, fewer than 60% complied with the CDC exercise recommendations, compared with at least 80% who reported almost no stress. Frequency of PA counseling of patients was consistently related to personal PA practices. The percent of students perceiving that PA counseling would be highly relevant to their practices decreased during the 4 yr of medical school, from 69 to 53% (P < 0.01). DISCUSSION: Among U.S. medical students, personal PA levels are higher than those of age-matched peers in the general population, are maintained throughout medical school, and are correlated with frequency of PA counseling of their patients. Promotion of adequate PA habits during medical education may be an important step to improve the PA preventive counseling that future clinicians provide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".