Formal mentorship in a surgical residency training program: A prospective interventional study
Bibliographic record
Abstract
BACKGROUND: Otolaryngology-Head and Neck surgery resident physicians (OHNSR) have a high prevalence of burnout, job dissatisfaction and stress as shown within the literature. Formal mentorship programs (FMP) have a proven track record of enhancing professional development and academic success. More importantly FMP have an overall positive impact on residents and assist in improving job satisfaction. The purpose of the study is to determine the effects of a FMP on the well-being of OHNSR. METHODS: A FMP was established and all OHNSR participation was voluntary. Eight OHNSR participated in the program. Perceived Stress Survey (PSS) and the Maslach Burnout Inventory (MBI) were administered at baseline and then at 3, 6, 9, and 12 month intervals. World Health Quality of Life-Bref Questionnaire (WH-QOL) was administered at baseline and at 12 months. RESULTS: Baseline statistics found a significant burden of stress and burnout with an average PSS of 18.5 with a high MBI of 47.6, 50.6, and 16.5 for the emotional, depersonalization, and personal achievement domains respectively. Quality of life was also found to be low with a WH-QOL score of 71.9. After implementation of the FMP, PSS was reduced to 14.5 at 3 months (p = 0.174) and a statistically significant lower value of 7.9 at 12 months (p = 0.001). Participants were also found to have lower emotional scores (14.9, p < 0.0001), levels of depersonalization (20.1, p < 0.0001), and higher personal achievement (42.5, p < 0.0001) on MBI testing at 12 months. Overall quality values using the WH-QOL was also found to be significantly improved (37.5, P = 0.003) with statistically significant lower scores for the physical health (33.9, p = 0.003), psychological (41.1, p = 0.001), social relationship (46.9, p = 0.019), and environment (53.5, p = 0.012) domains. CONCLUSION: This is the first study to show that FMP can potentially alleviate high levels of stress and burnout within a surgical residency program and achieve higher levels of personal satisfaction as well as overall quality of life.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".