Bibliographic record
Abstract
Abstract Good communication in multilingual healthcare settings is vital to delivering patient-centered care. This study investigated the communication experiences of physicians’ and patients at Zamboanga City Medical Center (ZCMC), a public hospital in a multilingual city in the Philippines, focusing on the linguistic dynamics among Chabacano, Bisaya, and Tausug speakers during medical consultations. Employing a qualitative-ethnographic design, the study utilized direct observations to analyze interactions guided by the Calgary-Cambridge Model. The study showed that the physicians’ and patients’ communication experiences were marked by their multilingual reality through the use of translanguaging — a dynamic use of multiple languages — tailored to accomplish specific communicative tasks during consultations. Physicians and patients used translanguaging, regardless of whether they shared a common language, to facilitate better comprehension and engagement. The study highlights the critical need for integrating multilingual competencies into healthcare and recommends transforming health institutions such as ZCMC into a patient-centered space, by providing policies for inclusive communication. In doing so, health institutions can improve patient communication experiences, advancing health equity and universal healthcare goals in linguistically diverse regions.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".