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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".