Integrating Human-Centered Design Methods Into a Health Promotion Project: Supplemental Nutrition Assistance Program Education Case Study for Intervention Design
Bibliographic record
Abstract
BACKGROUND: Human-centered design, or design thinking, offers an extensive toolkit of methods and strategies for user-centered engagement that lends itself well to intervention development and implementation. These methods can be applied to the fields of public health and medicine to design interventions that may be more feasible and viable in real-world contexts than those developed with different methods. OBJECTIVE: The design team aimed to develop approaches to building food skills among caregivers of children aged 0-5 years who are eligible for a federal food assistance program while they were in the grocery store. METHODS: They applied 3 specific human-centered design methods-Extremes and Mainstreams, Journey Mapping, and Co-Creation Sessions-to collaboratively develop intervention approaches to enhance Supplemental Nutrition Assistance Program Education (SNAP-Ed) reach and impact across food retail settings. Extremes and Mainstreams is a specific kind of purposive sampling that selects individuals based on characteristics beyond demographics. Journey Mapping is a visual tool that asks individuals to identify key moments and decision points during an experience. Co-Creation Sessions are choreographed opportunities for individuals to explicitly contribute to the design of a solution alongside research or design team members. RESULTS: Ten caregivers with diverse lived experiences were selected to participate in remote design thinking workshops and create individual journey maps to depict their grocery store experiences. Common happy points and pain points were identified. Nine stakeholders, including caregivers, SNAP-Ed staff, and grocery store dieticians, cocreated 2 potential intervention approaches informed by caregivers' experiences and needs: a rewards program and a meal box option. CONCLUSIONS: These 3 human-centered design methods led to a meaningful co-design process where proposed interventions aligned with caregivers' wants and needs. This case study provides other public health practitioners with specific examples of how to use these methods in program development and stakeholder engagement as well as lessons learned when adapting these methods to remote settings.
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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 | 0.042 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".