Patient-provider Sex and Race/Ethnicity Concordance
Bibliographic record
Abstract
BACKGROUND: Increasing patient-provider sex and race/ethnicity concordance has been proposed to improve healthcare and help mitigate health disparities, but the relationship between concordance and health outcomes remains unclear. OBJECTIVE: To examine associations of patient-provider sex, race/ethnicity, and dual concordance with healthcare measures. RESEARCH DESIGN AND PARTICIPANTS: Analyses of data from adult respondents indicating a usual source of healthcare (N=22,440) in the 2002 to 2007 Medical Expenditure Panel Surveys (each a 2-year panel). MEASURES: Year 1 provider communication, sex-neutral (colorectal cancer screening, influenza vaccination) and sex-specific (mammography, Papanicolaou smear, prostate-specific antigen) prevention; and year 2 health status (SF-12). Analyses adjusted for patient sociodemographics and health variables, and healthcare provider (usual source of care) sex and race/ethnicity. RESULTS: Of 24 concordance assessments, 3 were statistically significant. Women with female providers were more likely to report mammography adherence [average adjusted marginal effect=3.9%, 95% confidence interval (CI): 1.6%, 6.2%; P<0.01]. Respondents reporting dual concordance were less likely to rate provider communication in the highest quartile (average adjusted marginal effect =-4.2%, 95% CI: -8.1%, -0.2%; P=0.04), but dual concordance was associated with higher adjusted SF-12 Physical Component Summary scores (0.58 points, 95% CI: 0.00, 1.15; P=0.05). CONCLUSIONS: Little evidence of clinical benefit resulting from sex or race/ethnicity concordance was found. Greater matching of patients and providers by sex and race/ethnicity is unlikely to mitigate health disparities.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".