Co-design and evaluation of a regional and rural translation bronchiolitis platform: study protocol
Bibliographic record
Abstract
BACKGROUND: Bronchiolitis is the leading cause of hospitalisation in infants under one year. While evidence-based guidelines exist, including the updated Australasian Bronchiolitis Guidelines (2025), variations in care persist. Improving care in regional and rural hospitals is challenging due to limited access to paediatric expertise and evidence-based resources, contributing to continued use of low-value interventions. METHODS: This mixed methods study will use a human-centred design approach to co-design and evaluate the usability of the Regional and Rural Translation Bronchiolitis (RART-Bronch) platform, an interactive online tool targeted at non-metropolitan settings. The platform will feature bronchiolitis educational resources, implementation support, a benchmarking and feedback tool, and family education materials. Participants will be regional and rural clinicians and parents of infants hospitalised with bronchiolitis in these settings. Data will be collected through co-design meetings, usability surveys, semi-structured interviews, and think aloud methods. Engagement with the co-design process will also be evaluated. RESULTS: We will develop a user-friendly, evidence-based platform specific to regional and rural contexts that supports bronchiolitis guideline adherence and enhances clinical decision-making. CONCLUSION: Effectiveness will be evaluated in a future cluster randomised controlled trial and may inform future implementation strategies for improving care, quality and equity across regional and rural healthcare. IMPACT: This study aims to reduce variation in bronchiolitis care by co-designing an interactive online platform, RART-Bronch, with clinicians and parents from regional and rural settings. The platform aims to support sustainable reductions in low-value care and improve outcomes for infants with bronchiolitis. It will deliver evidence-based guidance to clinicians working in regional and rural hospitals with limited paediatric expertise. Findings will inform future implementation of tailored education, audit and feedback strategies in acute care. This work aims to enhance paediatric care quality in under-resourced settings and contribute to scalable, equitable improvements in bronchiolitis management across Australia.
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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 | 0.098 | 0.108 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".