Assessing culturally competent diabetes care with unannounced standardized patients.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: More effective diabetes care is desperately needed, especially for ethnic minority populations. Provider cultural competence promises to be an important means for reducing disparities in outcomes for patients with diabetes. The objectives of this study were to understand the role of cultural competence in the diabetes office visit. METHODS: Unannounced standardized patients (SPs) were sent to the offices of 29 family and internal medicine residents and practicing physicians. The SPs portrayed a Mexican American woman newly diagnosed with type 2 diabetes. Using a checklist developed with the input of experts in Hispanic/Latino health care and cultural competence, the SPs evaluated physicians' cultural competence, diabetes care, and general communications skills. RESULTS: The average total SP Checklist score was 70.7-11.0%, with a range of 43.9% to 90.2%. Physicians scored highly on items that measured general communication skills (95.9%) but were less likely to ask about social history (ie, family and community support issues, 51.9% and 48.1%, respectively). Sixty-seven percent of physicians ordered a hemoglobin A1c, 44% referred to ophthalmology, and 15% performed a monofilament exam. Physicians' inquiry into SPs explanatory model of disease (ie, asking about the SPs' views regarding their disease and its treatment) correlated with the performance of several diabetes treatment-related behaviors, Spearman's rho=.466. CONCLUSIONS: The findings provide support for a relationship between inquiry into patients' explanatory models of disease and effective diabetes care. Social history and explanatory model elicitation skills are vital parts of cultural competence training programs and potentially valuable tools for mitigating 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".