Combining First Nations Research Methods with a World Health Organization Guide to Understand Low Childhood Immunisation Coverage in Children in Tamworth, Australia
Bibliographic record
Abstract
In Australia, we used the World Health Organization’s Tailoring Immunization Programmes to identify areas of low immunisation coverage in First Nations children. The qualitative study was led by First Nations researchers using a strength-based approach. In 2019, Tamworth had 179 (23%) children who were overdue for immunisations. Yarning sessions were conducted with 50 parents and health providers. Themes that emerged from this research included: (a) Cultural safety in immunisation services provides a supportive place for families, (b) Service access could be improved by removing physical and cost barriers, (c) Positive stories promote immunisation confidence among parents, (d) Immunisation data can be used to increase coverage rates for First Nations children. Knowledge of these factors and their impact on families helps ensure services are flexible and culturally safe.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".