Bridging Implementation Science and Human-Centered Design: Developing Tailored Interventions for Healthier Eating in Restaurants
Bibliographic record
Abstract
Restaurants are important institutions in the communities' economy with the potential to promote healthier foods but have been under-engaged in public health nutrition efforts. In particular, independently owned, minority-serving and minority-owned restaurants, remain under-represented in nutrition promotion efforts despite disproportionate burdens of diet-related health outcomes among minority populations. Addressing this gap in engagement, we undertook a process of co-designing and implementing healthy eating-focused interventions in two Latin American restaurants in New York City, combining the Behavior Change Wheel intervention development framework with a Human-Centered Design approach. Restaurant owners and chefs were involved in the research synthesis and solution development processes, resulting in two tailored interventions. This paper describes this co-development process and offers reflections and lessons regarding: (1) implementation research in community settings, (2) the application of Human-Centered Design to promote the uptake of community-based interventions on food and health equity, and (3) the combined use of Human-Centered Design and Implementation science in these complex community settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.181 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".