Academic benchmarks for leaders in Otolaryngology - Head & Neck Surgery: A Canadian perspective
Bibliographic record
Abstract
BACKGROUND: The present study summarizes the demographics, subspecialty training, and academic productivity of contemporary leaders in Canadian Otolaryngology - Head & Neck Surgery (OHNS) training programs across Canada. METHODS: Demographic data regarding chairpersons (CPs) and program directors (PDs) were obtained from publicly-available faculty listings and online sources, and included employment institution, residency training, fellowship training status, gender, and years of post-graduate experience. Research productivity was measured using the h-index and number of publications, obtained from Scopus. Characteristics of CPs and PDs were compared using statistical analysis. RESULTS: Cross-sectional data was obtained from a total of 27 CPs and PDs from 13 accredited OHNS training programs across Canada active on July 1, 2019. All academic leaders completed at least 1 year of fellowship training. Head and neck oncology represented 77% of CPs and 59% of academic leaders overall, while pediatric otolaryngology represented 43% of PDs. Females represented 11% of academic leaders. There was a significant association between location of residency training and employment, with 56% (15/27) of physicians working where they had trained (p = 0.001, Fisher's exact test; φ = 2.63, p = 0.001). On average, individuals with a graduate (Master's) degree had a significantly higher H-index (17.7 vs 7.4, p = 0.001) and greater number of publications (106 vs. 52, p = 0.02). Compared to PDs, CPs had a significantly higher average h-index score (14.5 vs. 8.14, p = 0.04) and accrued more years of post-graduate experience (29.7 vs. 21.3 years, p = 0.008). There were no differences in the proportions of CPs and PDs with graduate degrees. There appeared to be a decline in research productivity beginning 3 years after academic appointment. CONCLUSIONS: This cross-sectional overview of academic leaders in Canadian OHNS programs demonstrates the following key findings: 1) all leaders completed fellowship training; 2) head and neck surgical oncology was the most common fellowship training subspecialty; 3) leaders were likely to be employed at the institution where they trained; 4) a Master's degree may be associated with increased research productivity; 5) there is a potential risk of decreased productivity after appointment to a leadership position; and 6) women are underrepresented in academic leadership roles.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".