Otolaryngology - Head and Neck Surgeon unemployment in Canada: A cross-sectional survey of graduating Otolaryngology - Head and Neck Surgery residents
Bibliographic record
Abstract
OBJECTIVE: Recently graduated Otolaryngology - Head and Neck Surgeons (OTO-HNS) are facing an employment crisis. To date, there has been no systematic evaluation of the factors contributing to this situation, graduating OTO-HNS trainee employment rates, nor the employment concerns of these graduating residents. This investigation sought to empirically evaluate prospective OTO-HNS graduate employment, identify factors contributing to this situation, and provide suggestions going forward. METHODS: A cross-sectional survey of the 2014 graduating cohort of OTO-HNS residents was conducted 6-months prior to graduation, and immediately following residency graduation. Surveyed items focused on the demographics of the graduating cohort, their future training and employment plans, and their concerns relative to the OTO-HNS employment situation. RESULTS: All twenty-nine Canadian medical school graduated OTO-HNS residents completed the initial survey, with 93% responding at the completion of residency. Only 6 (22%) indicated confirmed employment following residency training. 78% indicated that they were pursuing fellowship training. 90% identified the pursuit of fellowship training as a moderately influenced by limited job opportunities. The ability to find and secure full-time employment, losing technical skills if underemployed/unemployed, and being required to consider working in a less-desired city/province were most concerning. 34% of the residents felt that they were appropriately counseled during their residency training about employment. 90% felt that greater efforts should be made to proactively match residency-training positions to forecasted job opportunities. CONCLUSIONS: Canadian OTO-HN Surgeons lack confirmed employment, are choosing to pursue fellowship training to defer employment, and are facing startling levels of under- and unemployment. A multitude of factors have contributed to this situation and immediate action is required to rectify this slowly evolving catastrophe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".