Bridging the Gap in Facial Aesthetic Surgery Training: A National Survey Study of Canadian Resident and Program Director Perspectives on Resident-Run Clinics in Otolaryngology Residency Programs
Bibliographic record
Abstract
Introduction: Resident-run facial aesthetic surgery clinics improve surgical proficiency but are absent in Canadian Otolaryngology-Head and Neck Surgery (OtoHNS) training. Objectives and Hypotheses: To evaluate resident and program director (PDs) perspectives on facial aesthetic training and the feasibility of resident-run clinics. We hypothesized residents would report inadequate training and support for clinics, while directors would express caution due to logistical barriers. Study Design: National, cross-sectional survey. Methods: Anonymous, bilingual electronic surveys were distributed to Canadian OtoHNS residents and PDs. Quantitative data were analyzed descriptively; qualitative responses underwent thematic analysis. Results: Fifty residents and 11 PDs responded. Most residents (91%) reported performing ≤ 5 core procedures; 82% desired more exposure. Resident-run clinics were supported by 83% of residents but only 30% of PDs. PDs cited supervision, funding, and legal concerns. Conclusion: Significant training gaps exist. Resident-run clinics may enhance education but require structured supervision and institutional support.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".