Countering the “wrong story”: a Participatory Action Research approach to developing COVID-19 vaccine information videos with First Nations leaders in Australia
Bibliographic record
Abstract
Abstract The COVID-19 pandemic, coupled with the “infodemic” of misinformation, meant First Nations peoples in Australia’s Northern Territory were hearing “the wrong story” about COVID-19 vaccines. In March 2021, when the Australian government offered COVID-19 vaccines to First Nations adults there was no vaccine information designed with, or for, the priority group. To address this gap, we conducted a Participatory Action Research project in which First Nations leaders collaborated with White clinicians, communication researchers and practitioners to co-design 16 COVID-19 vaccine videos presented by First Nations leaders who spoke 9 languages. Our approach was guided by Critical Race Theory and decolonising processes including Freirean pedagogy. Data included interviews and social media analytics. Videos, mainly distributed by Facebook, were valued by the target audience because trusted leaders delivered information in a culturally safe manner and the message did not attempt to enforce vaccination but instead provided information to sovereign individuals to make an informed choice. The co-design production process was found to be as important as the video outputs. The co-design allowed for knowledge exchange which led to video presenters becoming vaccine champions and clinicians developing a deeper understanding of vaccine hesitancy. Social media data revealed that: sponsored Facebook posts have the largest reach; videos shared on a government branded YouTube page had very low impact; the popularity of videos was not in proportion to the number of language speakers and there is value in reposting content on Facebook. Effective communication during a health crisis such as the COVID-19 pandemic requires more than a direct translation of a script written by health professionals; it involves relationships of reciprocity and a decolonised approach to resource production which centres First Nations priorities and values.
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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 | 0.136 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".