The role of local languages in effective health service delivery
Bibliographic record
Abstract
A factor that impacts the health outcome of individuals is effective communication, and language is an important part of communication. The use of patients’ local language during health service delivery has been shown to influence patient satisfaction, compliance and overall health outcomes. A narrative review of existing literature was conducted, and the methodology involved searching through PubMed, Google Scholar, SCOPUS, Directory of Open Access Journals (DOAJ), COCHRANE Library, and African Journals Online (AJOL) focusing on original studies that examined the influence of indigenous languages on healthcare delivery. Also, only literature published in the English language was considered and narrative reviews, preprints, opinions, letters, and commentaries were excluded. Twenty studies were reviewed, and there were diverse categories of eligible populations among the papers included in this review: sixteen Quantitative Studies, two systematic reviews, and two mixed-method studies. The key findings showed that the use of local languages in healthcare delivery improves metrics such as patient satisfaction, compliance with medical instructions, and health improvement. The identified limitations of this study include the restrictions in the criteria for the literature that were reviewed, limited focus on specific healthcare specialties, and possible publication bias. The recommendations include implementing policies that prioritize local language use in healthcare service delivery, community engagement, and promotion of health technologies that support communication in multiple languages.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".