Conducting a child injury prevention RCT in the wake of COVID-19: lessons learned for virtual human subjects research
Bibliographic record
Abstract
Unintentional injury is the leading cause of death among children in the United States, and children living in low-income households are particularly at risk for sustaining unintentional injuries. Close parental supervision has been found to reduce young children's risk for injury; however, few studies have examined interventions to increase parental supervision. This paper discusses COVID-19 related modifications that were made to a federally funded randomized controlled trial to reduce low-income children's risk for unintentional injury. The study's procedures (data collection and intervention delivery) had to be transitioned from in-person to a fully virtual format. Modifications that were made to the study included use of: participant cell phones to conduct data collection and intervention sessions; virtual meeting software to conduct sessions with participants and; an online platform to collect questionnaire data. In addition, many modifications were required to complete the in-home observation virtually. In terms of feasibility, the investigators were able to collect all of the data that was originally proposed; however, recruitment and retention was more challenging than anticipated. Lessons learned during the modification process are included to provide guidance to researchers seeking to conduct virtual human subjects research in the future.
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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 | 0.236 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".