Positive mental health and burnout in first to fourth year medical students
Bibliographic record
Abstract
Background: Medical students are at risk of poor mental health and burnout compared to general population age- and education-matched peers, which has future implications for patient care. Research has suggested that demographic factors can predict mental illness and burnout among medical students. However, less is known about predictors of mental health and how they compare to predictors of burnout, and few studies have examined multiple demographics simultaneously. Objectives: This study examined and compared demographic predictors (gender, ethnicity, age, level of education, year of study and proposed specialty) of mental health and burnout in first to fourth year Canadian medical students. Method: Medical students ( n = 129) completed online surveys comprised of validated questionnaires. Results: Multiple regression indicated that third year (β = −.243, p = .013) negatively predicted mental health ( R 2 = 15.0%). Female gender (β = .242, p = .005), ‘other’ ethnicities (β = .189, p = .028), third year (β = .391, p < .001) and fourth year (β = .212, p = .023) positively predicted burnout ( R 2 = 32.7%). Female gender and fourth year predicted mental health and burnout differently. ‘Other’ ethnicity, second year and third year predicted mental health and burnout similarly. Conclusion: Findings fill gaps in the literature and may inform medical stakeholders in developing targeted programmes for improving medical students’ mental health and burnout. Medical students with greater well-being can progress into physicians who will be more likely to promote well-being in their patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".