Eliciting patient past experiences of healthcare discrimination as a potential pathway to reduce health disparities: A qualitative study of primary care staff
Bibliographic record
Abstract
OBJECTIVE: To understand whether and how primary care providers and staff elicit patients' past experiences of healthcare discrimination when providing care. DATA SOURCES/STUDY SETTING: Twenty qualitative semi-structured interviews were conducted with healthcare staff in primary care roles to inform future interventions to integrate data about past experiences of healthcare discrimination into clinical care. STUDY DESIGN: Qualitative study. DATA COLLECTION/EXTRACTION METHODS: Data were collected via semi-structured qualitative interviews between December 2018 and January 2019, with health care staff in primary care roles at a hospital-based clinic within an urban safety-net health system that serves a patient population with significant racial, ethnic, and linguistic diversity. PRINCIPAL FINDINGS: Providers did not routinely, or in a structured way, elicit information about past experiences of healthcare discrimination. Some providers believed that information about healthcare discrimination experiences could allow them to be more aware of and responsive to their patients' needs and to establish more trusting relationships. Others did not deem it appropriate or useful to elicit such information and were concerned about challenges in collecting and effectively using such data. CONCLUSIONS: While providers see value in eliciting past experiences of discrimination, directly and systematically discussing such experiences with patients during a primary care encounter is challenging for them. Collecting this information in primary care settings will likely require implementation of multilevel systematic data collection strategies. Findings presented here can help identify clinic-level opportunities to do so.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".