Achieving Universal Healthcare Coverage in a Multilingual Care Setting: Linguistic Diversity and Language Use Barriers as Social Determinants of Care in Ghana
Bibliographic record
Abstract
The Health Sustainable Development Goal (SDG3) focuses on achieving universal healthcare coverage (UHC) through people-centered primary care and access to affordable high-quality healthcare services, medicines/vaccines, and specialized care professionals without undue financial stress. However, achieving UHC can be challenging if healthcare providers and patients cannot communicate meaningfully. Severe language barriers affect access to healthcare services. This study explores how linguistic diversity and language use barriers impact person-centered care delivery and access to healthcare services in a multilingual Ghanaian healthcare setting. Data were collected through in-depth individual interviews with patients ( n = 17), caregivers ( n = 11), and nurses ( n = 11), one group interview with four patients, and participant observations. Data transcripts and field notes were inductively and manually coded and analyzed thematically. The study revealed that language barriers affect effective nurse–patient communication and interaction. Healthcare professionals and patients shop for translators and interpreters to overcome communication challenges. The study also found that healthcare professionals used medical jargon to emphasize their identity as experts despite its consequences on nurse–patient interactions and patient care. Miscommunication and misunderstanding due to language barriers derail nurse–patient therapeutic relationships and undermine patient disclosure, participation in the care process, and care quality, leading to adverse UHC outcomes. Therefore, serious attention must be paid to language use contingencies to achieve universal care, especially in resource-scared and multilingual healthcare contexts.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".