Using Talanoa in Community-Based Research with Australian Pacific Islander Women with Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes is a significant public health problem and Australian Pacific Islander (API) women and their communities are experiencing a higher burden of morbidity and mortality from the disease. Despite this higher burden there are few initiatives that are culturally tailored to improve prevention and management. OBJECTIVES: We used talanoa, a community-based research methodology to build capacity with API women living in Queensland and to develop culturally relevant methods of information sharing and knowledge building. METHODS: The partnership informed the co-design and conduct of research using a talanoa methodology framework. LESSONS LEARNED: Talanoa was used in negotiating the research partnership, setting up a steering committee, developing protocols for community engagement, collecting and co-constructing knowledge and disseminating community outcomes. CONCLUSIONS: The community-academia partnership and the participatory processes using talanoa facilitated dialogue and engagement to promote diabetes prevention and management for API communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".