Provision of Orthopaedic Workforce and Implications for Access to Orthopaedic Services in Ontario
Bibliographic record
Abstract
BACKGROUND: The aging population and increasing obesity rates will increase the prevalence of musculoskeletal conditions. Reports of orthopaedic surgeon shortages raise concerns about the ability of the health-care system to meet current and future demand in orthopaedics. A survey of all orthopaedic surgeons in Ontario, Canada, was carried out in 2006 to (1) update provision estimates of orthopaedic surgeons; (2) examine practice characteristics and perceived barriers to service; and (3) relate geographic availability of surgeons to population utilization of office-based and surgical orthopaedic services. METHODS: A two-part questionnaire was sent to all orthopaedic surgeons in Ontario in 2006. Provision data in hours per week and full-time equivalents and practice patterns were analyzed by health region. Population-based data on the use of orthopaedic services were obtained from health service administrative databases. RESULTS: There were 396 practicing orthopaedic surgeons in Ontario in 2006, equivalent to 2.43 full-time equivalents per 100,000 population, a finding similar to surveys in 1997 and 2000. Most surgeons were male, with a mean age of forty-nine years, with mainly adult practices; 48% reported having a subspecialty. Provision varied across Ontario, with an average of 112 hours per week of direct clinical time per 100,000 population (50% in the office, 30% in the operating room, 20% working on call). Many surgeons also reported time for administration, teaching, and research. Most respondents reported barriers to timely surgery, notably a lack of resources (operating room time, anesthesia, nursing, and/or bed capacity). Low orthopaedic provision was associated with lower utilization of office-based and surgical services, after controlling for neighborhood income and type of residence (urban or rural). CONCLUSIONS: Shortages and geographic variation in the supply of surgeons mean that access to care continues to be a challenge in Ontario. In regions with fewer surgeons, residents are more likely to be deprived of office-based services, potentially affecting access to surgery and to orthopaedic expertise. In light of a potential shortage of surgeons, alternative methods of service provision may be needed to respond to the aging of the baby boomer population and an anticipated growth in the demand for surgery.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".