Prevalence and relationship between burnout and depression in our future doctors: a cross-sectional study in a cohort of preclinical and clinical medical students in Ireland
Bibliographic record
Abstract
OBJECTIVES: This cross-sectional study was designed to measure burnout and its impact on risk of depression in a medical student population, comparing the preclinical and clinical years. DESIGN: We conducted a survey of 269 medical school students in both preclinical and clinical years at the Royal College of Surgeons in Ireland, using the Beck Depression Inventory-Fast Screen (BDI-FS), the Maslach Burnout Inventory-Student Survey and items assessing willingness to use mental health services. Burnout scores were calibrated to probability of depression caseness and classified as low risk (<25%), intermediate (25%-50%) and high risk (>50%) of depression. RESULTS: There was a 39% (95% CI 33% to 45%) prevalence of depressive caseness based on a score of ≥6 on the BDI-FS. Prevalence did not vary significantly between clinical and preclinical years. The rate of burnout varied significantly between years (p=0.032), with 35% in the high-burnout category in clinical years compared with 26% in preclinical years. Those in the low burnout category had a 13% overall prevalence of depressive symptoms, those in the intermediate category had a 38% prevalence and those in the high category had a 66% prevalence of depressive symptoms. Increasing emotional exhaustion (OR for one-tertile increase in score 2.0, p=0.011) and decreasing academic efficacy (OR 2.1, p=0.007) increased the odds of being unwilling to seek help for mental health problems (11%). CONCLUSION: While previous studies have reported significant levels of burnout and depression, our method of calibrating burnout against depression allows burnout scores to be interpreted in terms of their impact on mental health. The high prevalences, in line with previous research, point to an urgent need to rethink the psychological pressures of health professions education.
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.
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 | 0.018 | 0.004 |
| 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 | 0.002 |
| Research integrity | 0.001 | 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".