Association between Burnout Syndrome and medical training by specialty in first-year residents
Bibliographic record
Abstract
Introduction. Burnout syndrome (BOS) comprises emotional exhaustion, depersonalization, and reduced personal accomplishment in those affected. Instruments such as the Maslach Burnout Inventory (MBI) can help to identify those affected. Physicians in training have been described as an at-risk group for this syndrome. Objective. Describe the association between BOS and medical training by specialty in first-year residents. Method. This is a cross-sectional analytical study of specialty residents at the Hospital Civil de Guadalajara. Sociodemographic data were obtained and the MBI was administered to identify BOS. Samples were compared, and a comparative analysis performed to identify factors associated with BOS. Results. Eighty-eight residents were included, with 21.6% (n = 19) presenting BOS, 53.4% displaying emotional exhaustion (n = 47), 53.7% showing depersonalization (n = 47), and 39.8% reduced personal accomplishment (n = 35). Presenting BOS was not associated with sociodemographic characteristics or type of specialty. Work hours (ro = .229, p = .032), and a higher number of on-call hours/week (ro = .34, p = .001) were associated with higher BOS. Discussion and conclusion. The prevalence of BOS was lower than expected. Over half scored for emotional exhaustion and depersonalization, which could be explained by a self-reporting bias. There was no association between the group/type of specialty and BOS. This study creates new knowledge that works as an institutional situational diagnosis, helps to determine the scope of the problem, and encourages to consider the contributing factors to its origin and maintenance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".