‘Gudhurr-wutu’ (strengthen the mob): maximizing the impact of First Nations health and well-being messaging
Bibliographic record
Abstract
Dunghutti Country is located within Australia's number one 'stroke hotspot', with incidence almost double the national average. First Nations peoples are disproportionately affected by stroke, with higher incidence and hospitalization rates and a greater risk of dying compared to non-First Nations Australians. Early recognition of stroke symptoms is critical for people to access time-sensitive medical interventions, maximizing recovery potential. Whilst an internationally recognized F.A.S.T (Face, Arm, Speech, Time) message exists to promote rapid recognition of stroke symptoms, community awareness of F.A.S.T is limited. This project aimed to collaboratively design a culturally responsive F.A.S.T health message with and by First Nation's peoples, thus increasing awareness of stroke symptoms, to improve response for seeking time-sensitive medical care. Guided by a qualitative participatory action methodology, and the use of cross-cultural Yarning as the method, this collaborative project involved six Dunghutti stroke survivors, a Dunghutti artist, a knowledge holder of local Dunghutti language, and a local occupational therapist/researcher, who reviewed the mainstream F.A.S.T health message and reimagined that message in a culturally relevant and meaningful way. Yarning allowed exploration of key themes, identifying three elements necessary to maximize the impact of First Nations health and well-being messaging. Elements included ensuring the health message (i) connects to Country, (ii) connects to understandings of health and well-being, and (iii) connects through relevant content, with First Nations peoples centred within the ideation, development, and message delivery processes.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".