Operationalizing the Behaviour Change Wheel and APEASE criteria to co-develop recommendations with stakeholders to address barriers to school-based immunization programs
Bibliographic record
Abstract
INTRODUCTION: School-based immunization programs offer an accessible route to routine vaccines for students. During the COVID-19 pandemic, school closures to comply with public health measures had a drastic effect on school-based immunization program delivery and associated vaccine uptake. We sought to integrate findings from a mixed methods study to co-develop evidence-based and theory-informed recommendations with a diverse group of stakeholders (i.e., decision makers, healthcare providers, school staff, parents and adolescent students) to address barriers to new and existing school-based immunization programs. METHODS: Findings from a mixed methods study were integrated using a joint display and narrative summary. These findings were mapped through the Behaviour Change Wheel, a series of tools designed to facilitate the development of behaviour change interventions. Draft recommendations were provided to previous mixed methods study participants who consented to participating in future phases of the research study (n = 26). Feedback was captured using a Likert-scale survey of acceptability, practicality, effectiveness, affordability, safety and equity (APEASE) criteria, with feedback and additional insights captured using open-ended textboxes. Data was used to revise and finalize recommendations. RESULTS: Applying the Behaviour Change Wheel, we drafted 26 evidence-based, theory-informed recommendations to address barriers to school-based immunization programs. Participants (n = 16) provided feedback, with half of the recommendations scoring 80% or higher across all six APEASE criteria. The remaining 13 recommendations received a moderate score across one or more criteria. Stakeholders identified a high level of interest in expanding the use of e-consent forms, expanding programming to offer a meningitis B vaccine, and recommendations to ease student anxiety. CONCLUSION: We co-developed a range of recommendations to improve school-based immunization programs with stakeholders using data generated from a mixed methods study. Implementation of any single or combination of recommendations will need to be tailored to local clinic procedures, school system and health system resources.
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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 | 0.183 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".