Application of digital engagement tools for exception from informed consent community consultation and public disclosure in the pediatric prehospital airway resuscitation trial
Bibliographic record
Abstract
Background: Emergency care trials may require compliance with federal Exception from Informed Consent (EFIC) regulations, including community consultation (CC) and public disclosure (PD). The reach of traditional CC and PD modalities is limited. We describe the application of novel digital engagement tools to enrich CC and PD in a pediatric emergency care trial. Methods: In support of EFIC CC and PD efforts for the Pediatric Prehospital Airway Resuscitation Trial (Pedi-PART), a multicenter trial of paramedic airway management in critically ill children, we deployed two digital engagement tools: 1) social media advertisements, and 2) marketing research panels. We disseminated social media advertisements (Facebook and Instagram) describing the study to targeted users in 10 communities. We determined social media advertisement impressions and engagements (shares, reactions, saves, comments, likes and clicks). We also disseminated community surveys using a marketing research panel (Qualtrics Marketing Research Services), determining the number of completed surveys, time to achieve 200 surveys, demographics of survey respondents and percentage with supportive responses. Results: There were 23.3 million social media advertisement impressions (range 1.8-2.7 million per community) reaching 3.4 million unique users (range 239,494-439,360 per community) and resulting in 13,873 engagements (range 828-1,656 per community). Distribution of the community survey through the marketing research panel resulted in 6,771 completed surveys (range 531-914 per community). Across communities, time to 200 completed surveys ranged from 5-28 days. Survey respondents were 61.9% female, 27.0% minority race and 40.8% household income <$50,000. Most survey respondents (90.7%) supported the trial. Conclusions: Digital engagement tools efficiently reached a large and diverse population and yielded key community feedback to inform research trial deployment. Digital engagement tools offer valuable techniques to enrich EFIC CC and PD efforts.
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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 | 0.138 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".