Trust in Physicians and Racial Disparities in HIV Care
Bibliographic record
Abstract
Mistrust among African Americans is often considered a potential source of racial disparities in HIV care. We sought to determine whether greater trust in one's provider among African-American patients mitigates racial disparities. We analyzed data from 1,104 African-American and 201 white patients participating in a cohort study at an urban, academic HIV clinic between 2005 and 2008. African Americans expressed lower levels of trust in their providers than did white patients (8.9 vs. 9.4 on a 0-10 scale; p < 0.001). African Americans were also less likely than whites to be receiving antiretroviral therapy (ART) when eligible (85% vs. 92%; p = 0.02), to report complete ART adherence over the prior 3 days (83% vs. 89%; p = 0.005), and to have a suppressed viral load (40% vs. 47%; p = 0.04). Trust in one's provider was not associated with receiving ART or with viral suppression but was significantly associated with adherence. African Americans who expressed less than complete trust in their providers (0-9 of 10) had lower ART adherence than did whites (adjusted OR, 0.40; 95% CI, 0.25-0.66). For African Americans who expressed complete trust in their providers (10 of 10), the racial disparity in adherence was less prominent but still substantial (adjusted OR, 0.59; 95% CI, 0.36-0.95). Trust did not affect disparities in receipt of ART or viral suppression. Our findings suggest that enhancing trust in patient-provider relationships for African-American patients may help reduce disparities in ART adherence and the outcomes associated with improved adherence.
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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 | 0.003 | 0.018 |
| 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 | 0.001 |
| 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".