Formal Mentorship in Surgical Training: A Long-Term Prospective Interventional Study
Bibliographic record
Abstract
Objective Surgical training programs have a high prevalence of trainee stress and burnout. Formal mentorship programs (FMP) have been shown to alleviate these factors and improve quality of life (QOL) in short-term follow-up. This study aims to determine the long-term effects of an FMP on the well-being of a single-center cohort of surgical trainees. Methods A voluntary FMP was established at a surgical training program comprised 8 resident physicians. To quantitatively measure stress and burnout, the Perceived Stress Survey (PSS) and Maslach Burnout Inventory (MBI) were administered at baseline, 3, 6, 9, 12, 18, and 24 months. The World Health Quality of Life-Bref Questionnaire (WH-QOL) was administered at baseline, 12 and 24 months. Results Baseline levels of stress and burnout were high among all residents with an average PSS of 18.5 and MBI of 47.6, 50.6, and 16.5 for the emotional, depersonalization, and personal achievement domains respectively. After FMP implementation, PSS was reduced to 7.9 at 12 months ( P = .001). These scores were sustained at 24 months (6.8, P = 1). MBI scores improved in emotional exhaustion (14.9, P < .0001), depersonalization (20.1, P < .0001), and personal achievement (40.1, P < .0001) at 12 months. All these benefits were sustained at 24 months. Baseline overall WH-QOL scores reflected low QOL (71.9). These significantly improved at 12 months (37.5, P = .002) with further improvement by 24 months (17.2, P = .03). Conclusion Long-term follow-up of a previously successful academic surgical FMP showed lasting improvements in stress, burnout, and overall QOL, despite new life challenges.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".