Using human-centred design to tackle COVID-19 vaccine hesitancy for children and youth: a protocol for a mixed-methods study in Montreal, Canada
Bibliographic record
Abstract
INTRODUCTION: To successfully combat COVID-19 vaccine hesitancy and increase uptake, research has demonstrated that interventions are most effective when tailored to meet local needs through active engagement and co-development with communities. This mixed-methods project uses a human-centred design (HCD) approach to understand local perspectives of COVID-19 vaccine hesitancy and develop strategies to enhance vaccine confidence for children and adolescents. METHODS AND ANALYSIS: Project ECHO (Étude Communautaire sur l'Hésitation vaccinale contre la COVID-19) combines population-based surveys of parents and adolescents with community-based participatory action research to design and pilot strategies to enhance COVID-19 vaccine confidence in two underserved and ethnoculturally diverse neighbourhoods of Montreal, Canada. Two surveys conducted 6 months apart through primary and secondary schools are used to monitor vaccine acceptance and its social determinants among children and youth. Analyses of survey data include descriptive and inferential statistical approaches. Community-led design teams of parents and youth from the two participating neighbourhoods, supported by academic researchers, design thinking experts and community partners, use an HCD approach to: (1) gather data to understand COVID-19 vaccine decision-making among parents and youth in their community and frame a design challenge (inspiration phase); (2) develop an intervention to address the design challenge (ideation phase) and (3) pilot the intervention (implementation phase). Strategies to evaluate the community-led interventions will be co-developed during the implementation phase. ETHICS AND DISSEMINATION: This study has been approved by the research ethics boards of the Sainte-Justine University Hospital Centre and the University of Montreal. Community design teams will be involved in the dissemination of findings and the design of knowledge translation initiatives that foster dialogue related to COVID-19 vaccination for children and adolescents among community, school and public health stakeholders. Findings will be disseminated through peer-reviewed publications, conference presentations, community forums, policy briefs, and social media content.
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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 | 0.095 | 0.039 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".