Environmental attributable fractions in remote Australia: the potential of a new approach for local public health action
Bibliographic record
Abstract
OBJECTIVES: To determine local values for environmental attributable fractions and explore their applicability and potential for public health advocacy. METHODS: Using World Health Organization (WHO) values for environmental attributable fractions, responses from a practitioner survey (73% response rate) were considered by a smaller skills-based panel to determine consensus values for Kimberley environmental attributable fractions (KEAFs). Applied to de-identified data from 17 remote primary healthcare facilities over two years, numbers and proportions of reasons for attendance directly attributable to the environment were calculated for all ages and children aged 0-4 years, including those for Aboriginal patients. RESULTS: Of 150,357 reasons for attendance for patients of all ages, 31,775 (21.1%) were directly attributable to the environment. The proportion of these directly due to the environment was significantly higher for Aboriginal patients than others (23.1% v 14.6%; p<0.001). Of 29,706 reasons for attendance by Aboriginal children aged 0-4 years, 7,599 (25.6%) were directly attributable to the environment, significantly higher than for non-Aboriginal children aged 0-4 years (25.6% v 18.6%; p<0.001). CONCLUSIONS: By addressing environmental factors, 20% of total primary healthcare demand could be prevented and, importantly, some 25% of presentations by Aboriginal children. IMPLICATIONS: KEAFs have potential to monitor impact of local environmental investments.
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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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".