Bibliographic record
Abstract
Background Research suggests that racial/ethnic concordance (matching) between patients and physicians improves quality of care for minority patients by reducing discrimination in the clinical encounter. Objective Examine the impacts of patient and physician race/ethnicity, and racial/ethnic concordance, on primary care outcomes including blood pressure, tobacco use, and cholesterol screening and tobacco use counseling. Research Design Multivariate regression analysis of 8160 visits by white and minority patients to 661 primary care physicians using the 2001 to 2003 National Ambulatory Medical Care Survey. I estimated models based on physicians who see both white and minority patients and include physician fixed-effects to correctly measure the contribution of concordance. Results Conditional on accessing a primary care physician, patient race does not explain differences in rates of these guideline-recommended preventive screenings. Concordance is generally not an important predictor of outcomes, though it is associated with rates of cholesterol screening 2 to 3 times higher among black and Hispanic men compared with whites. In contrast, practice patterns vary quite markedly by physicians' race/ethnicity. Conclusions Given that physician race is a more powerful predictor of preventive screening than patient-physician concordance, minority patients may receive some guideline-recommended care at lower rates in concordant pairs. Addressing physician education and training to ensure practice that is consistent with preventive care guidelines may be important. Forms of discrimination in the clinical encounter thought to be modified by concordance do not appear to drive disparities in these outcomes.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.008 | 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".