Patient Experiences in a Linguistically Diverse Safety Net Primary Care Setting: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The patient-centered medical home model intends to improve patient experience and primary care quality. Within an urban safety net setting in Northern California, United States, these desired outcomes are complicated by both the diversity of the patient community and the care continuity implications of a residency program. OBJECTIVE: The objective of our study was to understand the patient experience beyond standardized satisfaction measures. METHODS: We conducted a qualitative study, interviewing 19 patients from the clinic (English-, Spanish-, or Mien-speaking patients). RESULTS: Some themes, such as the desire to feel confident in their doctor, emerged across language groups, pointing to institutional challenges. Other themes, such as distrust in care being provided, were tied distinctly to speaking a language different from one's provider. Still other themes, such as a sense of powerlessness, were related to cultural differences and to speaking a language (Mien) not spoken by staff. CONCLUSIONS: Findings illuminate the need to understand cultural behaviors and interactional styles in a diverse patient population to create a high-quality medical home.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".