Linguistic Services in Ambulatory Clinics
Bibliographic record
Abstract
A review of the literature reveals few studies that focus on the challenge of language barriers in primary care settings. Recognizing the need for a national consensus on cultural and linguistic standards for health care in the United States, the Office of Minority Health recently released a set of standards for culturally and linguistically appropriate services (CLAS). These standards were utilized to examine the linguistic services available at eight ambulatory care centers in a small New England state in an effort to determine compliance with recommended national standards. Although myriad studies have focused on provision of linguistically appropriate care in emergency rooms (ERs), few studies have specifically examined ambulatory care settings. Numerous strategies have been adopted by individual clinics in an attempt to deal effectively with linguistic barriers. Yet without clear national regulations and dedicated funding for interpreter services, a large spectrum of services exists. Survey data were obtained from on-site visits at select community health clinics to ascertain availability, need, and utilization of linguistic services for patients with limited English proficiency. The majority of patients served by the clinics surveyed were predominantly Spanish-speaking. Results reveal that although most of the clinics provided informal mechanisms of interpreter services, few directly addressed linguistic services as a component of culturally competent care.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".