Positive mental health and burnout in first to fourth year medical students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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 it