Trends in payments among male and female ophthalmologists in Ontario from 1992 to 2018.
Bibliographic record
Abstract
OBJECTIVE: To examine sex differences in Ontario Health Insurance Plan (OHIP) payments from 1992 to 2018. DESIGN: Population-based observational study. PARTICIPANTS: Ophthalmologists submitting claims to OHIP from 1992 to 2018. METHODS: Physician billing data over 27 years (1992-2018) were analyzed for yearly number of ophthalmologists, OHIP payments, distinct patients, and patient visits. RESULTS: Yearly median OHIP payments to female ophthalmologists were less than to male ophthalmologists with a gap ratio of 0.55 in 1992 to 0.73 in 2018. Stratifying by full-time equivalent (FTE), there was little difference in median payments between males and females for 1 FTE. Median female-to-male payments ratio varied from 0.80 to 1.16 for <1 FTE and 1.14 to 0.84 for >1 FTE from 1992 to 2018. Among female ophthalmologists, 72.7% and 52.9% were <1 FTE and 11.4% and 19.2% were >1 FTE in 1992 and 2018, respectively. In comparison, for male ophthalmologists, 35.7% and 45.6% were <1 FTE and 43.4% and 45.6% were >1 FTE in 1992 and 2018, respectively. Overall, male ophthalmologists had more patients and patient visits than female ophthalmologists, but there was little difference between male and female ophthalmologists for 1 and >1 FTE. The results for <1 FTE varied by year. CONCLUSIONS: Overall, female ophthalmologists have lower OHIP payments compared with males, but there was little difference for those stratified to 1 FTE. This overall payments difference by sex is largely explained by the higher proportion of <1 FTE females, lower proportion of >1 FTE females, and higher payments for >1 FTE males.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".