Mentorship in Otolaryngology Head and Neck Surgery: A Scoping Review
Bibliographic record
Abstract
IMPORTANCE: Mentorship is increasingly recognized as a critical part of training across the spectrum of trainees. While explored more in-depth in the literature of other medical specialties, mentorship remains a nascent topic in the Otolaryngology Head and Neck Surgery (OHNS) literature. OBJECTIVE: The objective of this study was to assess the current literature on mentorship in OHNS. DESIGN: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines was used and the methodology was registered on Open Science Framework (https://doi.org/10.17605/OSF.IO/X5FQ7). The Medline, EMBASE, and Web of Science databases were searched. Two authors independently selected studies, with the senior author resolving discrepancies. Study quality was assessed using the Oxford Centre for Evidence-Based Medicine levels of evidence. SETTING AND PARTICIPANTS: English language studies employing any methodology that involved mentorship of medical trainees and staff in OHNS were included from the inception of the database up to September 20, 2023. INTERVENTION OR EXPOSURES: Any form of mentorship. MAIN OUTCOME MEASURE: The primary outcome was the benefits of mentorship afforded to the mentee. RESULTS: From 415 unique articles identified, 45 studies were included. The median publication year was 2020 (IQR 6.5, range 1999-2023). The major themes of benefits from mentorship include improving residency uptake (n = 22), clinical competency and professionalism (n = 20), diversity and equity (n = 19), research productivity (n = 17), career planning and advancement (n = 17), and quality of life (n = 11). Other common themes included active mentorship (n = 29), near-peer mentorship (n = 13), and utilizing digital tools for mentorship (n = 6). CONCLUSION AND RELEVANCE: Mentorship in OHNS has seen a sharp increase in publications in recent years. There are numerous benefits to mentorship including improving residency uptake, diversity initiatives, clinical competency and professionalism, research productivity, career planning and advancement, as well as quality of life.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".