Orthopedic surgeons’ transition into full-time practice over the last 20 years: an analysis using Ministry of Health billing data
Bibliographic record
Abstract
Background: Underemployment is a reality for many new graduates, who accept locum or part-time work as an alternative to unemployment because of lack of opportunities. We sought to analyze orthopedic surgeons’ Ontario Health Insurance Program (OHIP) billing data over a 20-year period as a proxy of practice patterns and hypothesized that billing in the first 6 years of practice would be affected by underemployment and locum. Methods: We analyzed the annual average billing totals of orthopedic surgeons, broken down by year of graduation, year of billings, and number of surgeons billing in that year. We analyzed public census data of the Ontario population size as a proxy of orthopedic demand. Results: A 2019 cross-sectional analysis showed that around 15 surgeons per graduating year were billing in Ontario from the 1995 to 2016 cohorts, while 2017 and 2018 saw an increase to 30 and 36 actively billing surgeons, respectively. The number returned to more historical numbers in 2019, with 20 actively billing surgeons. For those surgeons billing in Ontario, billing trends have been roughly stable, with average billings increasing each year for the first 6 years in practice (p < 0.001). Year of graduation did not have an effect on the first 6 years of billings (p > 0.5). Billings were stable after 6 years in practice (p > 0.09). Conclusion: The Ontario health care system has not expanded to support more orthopedic surgeons despite the aging and growing population; despite our growing population, the number of surgeons being trained and retained has not matched this growth. Further research needs to be done to guide optimal health human resource decision-making.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 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".