Investigating the Feasibility of Remote Recruitment and Data Collection in the Context of an In-Restaurant Intervention Study
Bibliographic record
Abstract
Consumption of restaurant food is linked with increased energy intake and poor diet quality among children. Our ongoing cluster-randomized trial is designed to promote healthier eating among children in restaurants, with original plans to recruit families and collect data in local restaurants. Interactions with the first cohort were conducted remotely due to COVID-19, offering the opportunity to examine remote recruitment and data collection in the context of an in-restaurant intervention study. Parents with a 4-to-8-year-old child were recruited from 2 locations (1 intervention, 1 control) of a local, quick-service restaurant chain in Summer 2021. Study information was posted online and in-restaurant. Interested parents contacted study staff via text, phone, or email and completed screening, followed by an online baseline survey if eligible. Participants received study materials (frequent diner card and placemat) via mail. Intervention materials promoted healthful kids’ meals, and control materials promoted kids’ meals generally. Families returned to the same restaurant 6 times during a 2-month exposure period, where placemats were available, and frequent diner cards could be used. In November 2021, families returned for a final restaurant visit, submitting photos of their child's meal and completing a final online survey. Parents responded to study advertisements primarily via text (n = 61, 56% of inquiries) and 26 parents were recruited (17 intervention, 9 control). Twenty-one families (81%) completed final study procedures. Overall, parent comments about the study were positive, including statements such as “clear instructions” and “easy to complete.” Some recommended changes including “make the frequent diner card digital.” Recruitment was slower compared to in-person restaurant studies, but compliance and retention were high. For comparison, our in-person pilot research in this restaurant chain screened 134 families and enrolled 126 in one Summer, with retention just under 50%. Additional research is needed to maximize the feasibility of remote research in restaurants, perhaps leveraging successful remote data collection methods from the present study while returning to in-person recruitment when feasible. NIH R01HD096748.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".