Physician specialties classification.
Bibliographic record
Abstract
<div> Referrals from primary care physicians (PCPs) to specialists are a key function of the primary care system, enabling access to secondary and tertiary health care services. Since the early 2000s, Ontario has implemented substantial primary care practice reforms, however, PCP referral patterns have not been examined since reforms were implemented. We conducted a cross-sectional study in Ontario analyzing PCPs’ referral patterns to specialists from January 1 to December 31, 2019. Data from physician administrative and Ontario Health Insurance Plan (OHIP) billing databases were linked for 9,301 PCPs practicing comprehensive primary care with 11.8 million patients. We calculated referral rates per physician and built a multivariable Poisson regression model stratified by physician sex, recognizing that female and male PCPs practice primary care differently, to examine the association between PCP’s referral rates and their practice model. Subgroup analyses were conducted for medical, surgical, diagnostics and General Practitioner (GP) focused practice specialties. Overall, PCPs in fee-for-service practice models (females: 0.72, 95% CI 0.71–0.72, males 0.71 95% CI 0.71–0.72) and Family Health Groups (females: 0.90, 95% CI 0.90–0.91, males 0.85 95% CI 0.84–0.85) had lower adjusted relative referral rates compared to those in Family Health Teams (FHTs); a finding that was consistent across medical and surgical specialties. Younger, part-time PCPs, those practicing in urban areas, those with larger roster sizes and those affiliated with a large practice group showed higher adjusted referral rates. Female PCPs tended to be younger (average age 47.2 years vs. 54.1 years for males; SMD=0.56), work part-time (32.1% vs. 17.9% for males; SMD=0.33), had a smaller patient roster (average 1,097.8 rostered patients vs. 1,442.1 for males; SMD=0.44), and had higher unadjusted referral rates to specialists compared to male PCPs (32.9 vs. 29.9 per 100 rostered patients). PCPs’ referral patterns in Ontario vary by practice model and PCP’s sex. Future changes to primary care practices should account for their effects on referral volumes to specialists. </div>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.127 | 0.031 |
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; both teacher heads agree on what is shown here.
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".