Medical Student Mental Health and Wellness Pilot Study
Bibliographic record
Abstract
This study hopes to examine by way of electronic survey sources of stress, impact on perceived performance, impact on intentions to quit, perceived support and needs amongst the future physicians (medical students) currently at a medical school in Southwestern Ontario. The goal is to address learners from multiple campuses to analyze global wellness. Each cohort can be studied independently or comparatively to identify potential differences within the various populations, i.e. first year vs third year medical students. The importance of addressing physician mental health and wellness as early as in medical school has been shown through previous research. A study by Dyrbye et. al. found higher rates of psychological distress (stress, depression, and anxiety) amongst medical students in the U.S. and Canada compared to the general population and age-matched individuals (Dyrbye et. al., 2006). This type of burnout can negatively impact emotional wellness, professionalism in the workplace, and adversely affect patient care (Shanafelt et. al., 2002). The results of this study can potentially identify and inform future strategies to prevent burnout and tailor coping strategies for undergraduate medical students.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".