Reducing the risk of pandemic influenza in Aboriginal communities
Bibliographic record
Abstract
Context: Aboriginal people are particularly vulnerable to pandemic influenza A, H1N109. This was first recognized in the First Nations of Canada. There have been calls for close planning with Aboriginal people to manage these risks. This article describes the process and findings from preliminary community consultations into reducing influenza risk, including pandemic H1N1(09) swine influenza, in Aboriginal communities in the Hunter New England area of northern New South Wales, Australia. Issue: Consultation was conducted with 6 Aboriginal communities in response to the rapidly evolving pandemic and was designed to further develop shared understanding between health services and Aboriginal communities about appropriate and culturally safe ways to reduce the influenza risk in communities. Agreed risk mitigation measures identified in partnership are being introduced throughout Hunter New England area. Lessons learned: Five theme areas were identified that posed particular challenges to limiting the negative impact of pandemic influenza; and a number of potential solutions emerged from focus group discussions: (1) local resource person: local identified ‘go to ’ people are heard and trusted, but need to have an understanding of H1N109; (2) clear communication: information must be presented simply, clearly and demonstrating respect for local culture; (3) access to health services: sick people need to know where
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".