Gender, Racial and Immigration Pay Gaps in Canadian Family Medicine: Qualitative findings from a mixed-methods analysis
Bibliographic record
Abstract
Context: Pay gaps in medicine have been documented along gender, racial, and immigration identities. These pay gaps are well documented outside of Canada, but little empirical work has been done to understand pay gaps in Canada, in family medicine, or using an intersectional lens. Objective: Understand how the identity features of Ontario family physicians (e.g. gender, race, immigration, nation of medical training, parenting status, age) impact their income. Study Design and Analysis: Reflexive thematic analysis was used for the qualitative portion of a mixed-method explanatory sequential study. Previous quantitative work guided recruitment and data collection strategies. Dataset: Interview data from 36 family physicians working in Ontario, purposively recruited for diversity in the self-reported time scheduled for a typical intermediate assessment (A007) and identity features. Outcome Measures: Outcomes were qualitative descriptions of decisions, circumstances, and factors which impacted income opportunities. Results: 36 family physicians described ways in which their income was impacted by identity, with factors grouped in three categories: patient population, philosophy of practice, and circumstances of work. For example, women physicians were likely to note that patients expected more time for counselling. They were also more likely to provide in-office gynecological procedures (e.g. cervical sampling, IUD insertion) described as poorly re-imbursed for the time and resources required. Physicians who provided care in a language other than English or French or are racialized described patient expectations of them that did not concord with the time-limited system in Ontario, and sometimes described heightened patient need. Different philosophies of practice were described, with physicians who emphasized the income-generating functions of their work making choices to charge and collect block fees or fees for unremunerated services (e.g. sick notes) while others emphasized patient care and accessibility, sometimes sacrificing income to provide better care (e.g. offering virtual visits on request). Physicians aligned with the latter philosophy were predominantly women, often racialized, and sometimes immigrants to Canada. Conclusions: Attention to intersectional experience highlights how income is influenced by social location. It provides explanatory evidence for quantitative findings of various pay gaps among Canadian physicians.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".