Referring and Specialist Physician Gender and Specialist Billing
Bibliographic record
Abstract
Importance: While a gender pay gap in medicine has been well documented, relatively little research has addressed mechanisms that mediate gender differences in referral income for specialists. Objective: To examine gender-based disparities in medical and surgical specialist referrals in Ontario, Canada. Design, Setting, and Participants: This cross-sectional study included referrals for specialist care ascertained from Ontario Health Insurance Plan physician billings for fiscal year 2018 to 2019. Participants were specialist physicians who received new patient consultations from April 1, 2018, to March 31, 2019, and the associated referring physicians. Data were analyzed from April 2018 to March 2020, including a 12-month follow-up period. Exposures: Specialist and referring physician gender (female or male). Main Outcomes and Measures: Revenue per referral was defined based on an episode-of-care approach as total billings for a 12-month period from the initial consultation. Mean total billings for female and male specialists were compared and the differential divided into the portion owing to referral volume vs referral revenue. Difference-in-differences multivariable regression analysis was used to estimate gender-based differences in revenue per referral. For each referring physician, gender-based differences in referral patterns were examined using case-control analysis, in which specialists who received a referral were compared with matched control specialists who did not receive a referral. This analysis considered the gender of the specialist and concordance between the gender of the referring physician and specialist, among other characteristics. Results: Of 7 621 365 new referrals, 32 824 referring physicians, of whom 13 512 (41.2%) were female (mean [SD] age, 46.3 [11.6] years) and 19 312 (58.8%) were male (mean [SD] age, 52.9 [13.5] years), made referrals to 13 582 specialists, of whom 4890 (36.0%) were female (mean [SD] age, 45.6 [11.0] years) and 8692 (64.0%) were male (mean [SD] age, 51.8 [13.0] years). Male specialists received more mean (SD) referrals than did female specialists (633 [666] vs 433 [515]), and the mean (SD) revenue per referral was higher for males ($350 [$474]) compared with females ($316 [$393]). Adjusted analysis demonstrated a -4.7% (95% CI, -4.9% to -4.5%) difference in the revenue per referral between male and female specialists. Multivariable regression analysis found that physicians referred more often to specialists of the same gender (odds ratio, 1.04; 95% CI, 1.03-1.04) but had higher odds of referring to male specialists (odds ratio, 1.10; 95% CI, 1.09-1.11). Conclusions and Relevance: In this cross-sectional study of the gender pay gap in specialist referral income, the number and revenue from referrals received differed by gender, as did the odds of receiving a referral from a physician of the same gender. Future research should examine the effectiveness of different policies to address this gap, such as a centralized, gender-blinded referral system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".