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 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 | 0.073 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".