Engage, understand, listen and act: evaluation of Community Panels to privilege First Nations voices in pandemic planning and response in Australia
Bibliographic record
Abstract
INTRODUCTION: First Nations Peoples of Australia have not been included in the development nor prioritised in pre-2009 pandemic plans despite being a priority population in Australian health policy. Marginalised groups experience amplified barriers and systemic disadvantage in emergencies, however, their voices have not been heard in past pandemic responses. Through effective engagement with disadvantaged and oppressed groups, health authorities can gain a deeper understanding of how to design and implement pandemic control strategies. There have been limited studies with First Nations Peoples that has focused on pandemic planning and response strategies. Deliberative inclusive approaches such as citizens juries have been a way to uncover public perceptions. METHODS: Qualitative thematic research methods were used to conduct the study. We convened five First Nations Community Panels in three locations in Australia between 2019 and 2020. We used an Indigenist research approach, community-based Participatory Action Research framework and 'yarning' to understand whether Community Panels were an acceptable and appropriate way of engaging First Nations Peoples. Forty First Nations participants were purposively recruited through local and cultural networks. Panels heard evidence supporting various pandemic response strategies, and cross-questioned public health experts. RESULTS: All 40 participants from the 5 panels verbally indicated strong support of the Community Panels approach as an effective way of engaging First Nations Peoples in making decisions about pandemic planning and response strategies. The main theme of 'respect' centred on the overarching principle that First Nations Peoples are important in the context of continuation of culture and ongoing political resistance. CONCLUSION: First Nations Community Panels are a way of enabling active participation of First Nations peoples, increasing knowledge and understanding, and a way for government and policymakers to respectfully listen to First Nations opinions and values.
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 | 0.065 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".