Family physician pay inequality: a qualitative study exploring how physician responses to perceived patient expectations may explain gender, race, and immigration status pay differences
Bibliographic record
Abstract
BACKGROUND: Pay inequality related to social identity has been observed among physicians, even after accounting for hours worked and specialty. Physician identity factors, such as gender and race, may contribute to practice behaviours in ways that affect income. In this study, we sought to explore how Ontario family physicians understand the relation between their identities and practice patterns and to form a theory of how identities may influence practice decisions in ways that result in income disparities. METHODS: We conducted a constructivist grounded theory study to understand how social identities affect income discrepancies among physicians. We conducted interviews with family physicians practising in Ontario. Physicians were purposively and then theoretically sampled for variation on several identity factors. We staged the analysis using constant comparative techniques. RESULTS: Fifty-five family physicians participated. The analysis identified physician perception of patient expectations as a key factor influencing income. Based on the interviews, we developed a 4-stage theory to explain this mechanism: physician understanding of patient expectations, the nature of the expectations, physician responses to those expectations, and financial implications of those responses. We illustrate this theory with data from 2 frequently occurring examples: how physician gender influences income via patient expectations, and how physician culture, language, and immigrant or nonimmigrant status influence income via patient expectations. INTERPRETATION: Patient-centred care requires individualized approaches, yet common physician remuneration models fail to account for the time needed to provide these meaningful interactions. This dynamic may create structural disincentives for physicians who provide relational, emotionally intensive, or culturally tailored care, potentially reinforcing income disparities related to social identities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".