Investigating demographics, physical activity intensity, and sedentary behaviour as predictors of burnout in first to fourth year medical students
Bibliographic record
Abstract
Medical students are at an increased risk for burnout compared to the general population (Dyrbye et al., 2014). Research has identified certain demographics as predictors of burnout in medical students (Cecil et al., 2014; Dyrbye et al., 2007; Dyrbye & Shanafelt, 2016), but most research has not examined a combination of demographic variables in one model. Moreover, physical activity (PA) and sedentary behaviour are two modifiable risk factors for burnout (Naczenski et al., 2017; Sloan et al., 2013). However, less is known about how PA intensities and sedentary behaviour influence burnout in medical students. This research investigated how demographics (gender, ethnicity, age, level of education, year of study, proposed specialty) and health behaviours (mild, moderate, and vigorous PA, and sitting) predicted burnout in medical students. Medical students (N=129) completed surveys of validated questionnaires assessing demographics, PA, sitting, and burnout. Data were analysed using multivariate linear regression. Results showed that female gender (?=.221, p=.016), 'other' ethnicity (?=.185, p=.040), third year (?=.435, p=
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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".