Learning from COVID-19 communication with speakers of First Nations languages in Northern Australia: Yolŋu have the expertise to achieve effective communication
Bibliographic record
Abstract
Purpose Achieving effective communication about COVID-19 was recognised as crucial from the earliest stages of the pandemic. In the Northern Territory, where most First Nations residents primarily speak an Aboriginal language and few health staff share their languages and cultural backgrounds, achieving effective communication is particularly challenging. It is imperative that speakers of First Nations languages, who best understand their challenges and solutions, inform future health communication policy and practice. This study was conducted with one First Nations language group – Yolŋu 1 1Yolŋu: First Nations people from the North-East Arnhem Land region of northern Australia., from North-East Arnhem Land – to share their experiences of COVID-19 communication. Methods Through a culturally responsive qualitative approach, a team of Yolŋu and other researchers engaged with Yolŋu community members and educators, and with Balanda 2 2Balanda: one of the terms used by Yolŋu to refer to non-Indigenous people. (non-Indigenous) staff who were involved in communicating about COVID-19 with Yolŋu. Data collection included in-depth interviews with 37 participants (27 Yolŋu, 10 Balanda) in their preferred languages, collaborative critical review of COVID-19 resources in Yolŋu languages, and documented researcher observations and reflections. The design was informed by extensive previous collaborative work in this context using culturally congruent methods. Main findings This study identified grave limitations in communication about COVID-19 with Yolŋu. COVID-19 communication was dominated by outsider prepared messages shared through social media and radio, often focusing on directives about what to do without explaining why. Inadequate engagement of Yolŋu in planning and implementation contributed to communication failure. Participants also identified how effective communication can be achieved: engaging local leaders and knowledge authorities at the outset to identify and implement locally relevant and feasible solutions; collaborative development of in-depth explanations matched to what Yolŋu want and need to know to make informed decisions; and face-to-face, ongoing communication in local languages by local educators, using communication processes aligned with Yolŋu cultural protocols and preferences. Principal conclusions Yolŋu have cultural knowledge, authority and processes to respond to health crises and communication challenges. However, during the COVID-19 pandemic, dominant culture health communication processes and priorities were privileged. Persisting with communication approaches that are not informed by relevant and available evidence is unethical and ineffective. Sustained community led approaches to health communication, supported by health services and systems, are crucial to achieve effective health communication with speakers of First Nations languages beyond the COVID-19 pandemic.
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 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".