Assessment of the current Canadian rhinology workforce
Bibliographic record
Abstract
BACKGROUND: The Canadian Rhinologic workforce and future needs are not well defined. The objective of this study was to define the current demographics and practice patterns of the Canadian Rhinologic workforce. Outcomes from this study can be used to perform rhinologic workforce needs assessments. METHODS: A national survey was administered to all Canadian otolaryngologists who were identified to have a clinical practice composed of >50% rhinology. RESULTS: 42 surgeons participated in the survey (65% response rate). The mean age was 46 (SD 10.1) years and the average age of planned retirement was 66 (SD 4.0). Eighty three percent of respondents had completed a rhinology fellowship and 17% practiced exclusively rhinology. Thirty three percent hold advanced degrees. Forty two percent of surgeons felt their access to operative time was insufficient. Six percent of surgeons reported not having access to image guided surgery. Fourteen percent felt that there were too many practicing rhinologists in Canada while 17% believed there were too few practicing rhinologists. Seventeen percent have advised their residents to pursue other fields due to a perceived lack of future jobs. Overall, 66% of respondents were satisfied with their income, and 83% were satisfied with their careers. CONCLUSIONS: This study has demonstrated that there is a perceived mismatch between the current supply of Rhinology labor and the capacity to treat patients in a timely manner. Outcomes from this study will begin to improve Rhinologic workforce planning in Canada and reduce the gap between patient demand and access to high quality care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".