Is Sociodemographic Status Associated with Empathic Communication and Decision Quality in Diabetes Care?
Bibliographic record
Abstract
OBJECTIVE: To assess the relationship between empathic communication, shared decision-making, and patient sociodemographic factors of income, education, and ethnicity in patients with diabetes. RESEARCH DESIGN AND METHODS: This was a cross-sectional study from five primary care practices in the Greater Toronto Area, Ontario, Canada, participating in a randomized controlled trial of a diabetes goal setting and shared decision-making plan. Participants included 30 patients with diabetes and 23 clinicians (physicians, nurses, dietitians, and pharmacists), with a sample size of 48 clinical encounters. Clinical encounter audiotapes were coded using the Empathic Communication Coding System (ECCS) and Decision Support Analysis Tool (DSAT-10). RESULTS: The most frequent empathic responses among encounters were "acknowledgement with pursuit" (28.9%) and "confirmation" (30.0%). The most frequently assessed DSAT components were "stage" (86%) and knowledge of options (82.0%). ECCS varied by education (p=0.030) and ethnicity (p=0.03), but not income. Patients with only a college degree received more empathic communication than patients with bachelor's degrees or more, and South Asian patients received less empathic communication than Asian patients. DSAT varied with ethnicity (p=0.07) but not education or income. White patients experienced more shared decision-making than those in the "other" category. CONCLUSIONS: We identified a new relationship between ECCS, education and ethnicity, as well as DSAT and ethnicity. Limitations include sample size, heterogeneity of encounters, and predominant white ethnicity. These associations may be evidence of systemic biases in healthcare, with hidden roots in medical education.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".