Using Human-Centered Design in Community-Based Public Health Research: Insights from the ECHO Study on COVID-19 Vaccine Hesitancy in Montreal, Canada
Bibliographic record
Abstract
(1) Background: This study used human-centered design (HCD) within a community-based research project to collaboratively develop local strategies aimed at enhancing COVID-19 vaccine confidence among children and youth. (2) Methods: HCD projects were carried out between December 2021 and August 2022 by four community-based design (CBD) teams in Montreal, Canada. The CBD teams were composed of parent and youth community members, public health and social science researchers, and HCD specialists. Process evaluation data, collected from the CBD team members through focus group discussions and written questionnaires, were used to reflect on the use of HCD in this project. (3) Results: The CBD teams designed and implemented projects addressing factors they identified as contributing to COVID-19 vaccine hesitancy for children and youth in their communities, including misinformation, lack of trust, social inequities, and resistance to pandemic-related restrictions. The CBD team members appreciated many aspects of the HCD approach, especially the values it stands for, such as empathy, co-creation, and collaboration. HCD and public health specialists described some tension between the different disciplinary approaches. (4) Conclusions: HCD holds promise for addressing complex public health issues, though further exploration of strategies for integrating HCD within established models of community-based public health research is needed.
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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 | 0.068 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".