Using provider–parent strategies to improve influenza vaccination in children and adolescents with special risk medical conditions: a randomised controlled trial protocol
Bibliographic record
Abstract
INTRODUCTION: Influenza immunisation is a highly cost-effective public health intervention. Despite a comprehensive National Immunisation Program, influenza vaccination in children and adolescents with special risk medical conditions (SRMCs) is suboptimal. Flutext-4U is an innovative, multi-component strategy targeting paediatric hospitals, general practice and parents of children and adolescents with SRMC. The Flutext-4U study aims to assess the impact of Flutext-4U to increase influenza immunisation in children and adolescents with SRMC. METHODS AND ANALYSIS: (hospital vaccine availability, ease of access and reminders for WCH subspecialists) with randomisation stratified by age-group (<5, 5-14, >14 to <18 years).The primary outcome is influenza vaccination, as confirmed by the Australian Immunisation Register.The proportion vaccinated (primary outcome) will be compared between randomised groups using logistic regression, with adjustment made for age group at randomisation. The effect of treatment will be described using an OR with a 95% CI. ETHICS AND DISSEMINATION: The protocol and all study materials have been reviewed and approved by the Women's and Children's Health Network Human Research Ethics Committee (HREC/20/WCHN/5). Results will be disseminated via peer-reviewed publication and at scientific meetings, professional and public forums. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12621000463875).
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.009 |
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".