Language ecology and health care: language varieties and communication in a latino-serving family health center
Bibliographic record
Abstract
Research on Hispanics/Latinos over the past decade with respect to the role of language and culture in access to and experiences with health care has focused on three primary variables, or factors. These are (1) the language spoken by the patient, physician, and other providers, including interpreters; (2) culturally-based beliefs about health and disease and the potential impact of these beliefs on experiences with health care systems; and (3) the role and effects of socio-demographic characteristics, such as socio-economic status and having (or not having) health insurance. What is lacking in this research is a more detailed analysis of the first factor, i.e., the language(s) spoken by patients and providers in clinical settings. What is especially important to investigate is the way(s) in which language choice and use are experienced by patients and family members, and how those experiences are understood and interpreted by participants in the health care delivery system, i.e., physicians, nurses, technicians, social workers. That is the focus of this empirical investigation of a Latino-serving family medical center located in San Antonio, Texas. Five hundred clinic patients completed surveys and about 100 of them were interviewed. Medical and non-medical support staff were interviewed as well. Results indicate a high level of satisfaction with quality of health care provided, but concerns were raised about communication problems at various points of contact in the delivery of health care services. Recommendations include hiring a full-time interpreter and providing classes in medical terminology in Spanish for clinic personnel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".