Language as a pillar of cultural safety: evaluating hospital-based healthcare workers’ knowledge of First Nations languages and interpreter services in East Arnhem Land, Australia
Bibliographic record
Abstract
BACKGROUND: Communication is a key determinant of health, yet in Australia’s Northern Territory (NT), First Nations language speakers usually receive medical care in English. The NT is a region of exceptional language diversity and vitality, with approximately 50 distinct First Nations languages spoken. At Gove District Hospital in East Arnhem Land, NT, 91% of First Nations peoples primarily speak an ancestral language, chiefly Yolŋu Matha, with a smaller proportion speaking Anindilyakwa, Burarra and others. Many would benefit from access to an interpreter during healthcare encounters. This study aimed to assess healthcare provider knowledge of local languages, and their use of professional interpreters. METHODS: An exploratory, electronic, anonymous survey was distributed to clinical staff at Gove District Hospital, capturing demographics, knowledge of language names and dialects, confidence in pronunciation and spelling, and interpreter use. A purposeful sampling strategy targeted doctors, nurses, and allied health professionals. Exclusions comprised employment for < 1 month, employment in an administrative role, as an Aboriginal Liaison Officer, or students. Responses were analysed using descriptive statistics and qualitative template analysis. RESULTS: Of an estimated 100 eligible staff, 56 participated (33 nurses, midwives, managers; 16 doctors; 7 allied health). Almost all (96%) identified at least one major local language, but only 18% felt confident pronouncing names and few could name dialects. The language identified correctly most often was Yolŋu Matha. 50% identified Anindilyakwa and none named Burarra. Spellings varied widely. While staff acknowledged the benefit of using interpreters, < 60% had used one, and just over half knew how to book one. Reported barriers included interpreter unavailability and time pressures. Reliance on Aboriginal Liaison Officers or family members raised concerns about confidentiality and accuracy. Staff expressed frustration at limited interpreter access, and a desire for training in local languages. CONCLUSIONS: Findings reveal substantial gaps in linguistic knowledge and interpreter utilisation in a region of major language diversity. Addressing these issues requires systemic and individual change, including accurate language documentation in health records, employing interpreters, and intercultural communication training. Recognising and respecting patients’ first languages is central to culturally safe care, and essential for improving health equity for First Nations peoples.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".