The Canadian Otolaryngology–Head and Neck Surgery Workforce in the Urban‐Rural Continuum: Longitudinal Data from 2002 to 2013
Bibliographic record
Abstract
Objectives To evaluate the proportion of otolaryngology–head and neck surgery (OHNS) providers who are rural versus urban based from 2002 to 2013. Secondary objective was to present perspectives of rural primary care providers on unmet needs for OHNS services. Study Design Mixed methods database analysis and prospective survey. Setting National administrative database. Subjects and Methods The Canadian Medical Association OHNS provider Masterfile and the Statistics Canada postal code file were used to determine provincial, urban, rural, and Aboriginal group care coverage. The Society of Rural Physicians of Canada was surveyed to explore care delivery and unmet needs for OHNS and audiology. Descriptive statistics and linear regression were used to describe results. Results Ontario and Quebec had the largest annual OHNS physician growth (6.38 providers/year; r2 = 0.94) versus stagnant growth in the territories. The clear majority of OHNS providers are in urban centers, and rural OHNS coverage is decreasing annually (–0.33 providers/year, r2 = 0.28). There are no OHNS providers in 485 population centers where Aboriginal groups are located. A survey of 40 rural primary care providers reported that OHNS care is most commonly delivered through seasonal visits to a local facility, with otology (hearing loss, chronic ear disease) and rhinology (nonmalignant nasal or sinus conditions) as the most frequently reported unmet needs. Conclusion From 2002 to 2013, OHNS coverage showed a trend for urban consolidation. Most Aboriginal groups may have decreased access to care, as there are no OHNS providers in 485 population centers where reserves are located. There is an unmet need for specialized OHNS services reported by rural primary care physicians, especially otology and rhinology.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".