Using Social Media for the Prevention of Pediatric Burn Injuries: Pilot Design and Usability Study
Bibliographic record
Abstract
BACKGROUND: Most pediatric burn injuries are preventable. Social media is an effective method for delivering large-scale messaging and may be useful for injury prevention in this domain. OBJECTIVE: This study evaluates the feasibility of creating a social media campaign for pediatric burn injury prevention. METHODS: Ad spots containing a headline, short introduction, and video were created and posted on Facebook and Instagram over 4 months. Ad spots were targeted to parents and caregivers of children in our region with the highest number of burn injuries. We assessed the impact of each ad set using ThruPlays, reach, and video plays. RESULTS: We created 55 ad spots, with an average length of 24.1 (range 10-44) seconds. We reached 26,496 people during the campaign. The total ThruPlays of the 55 ad spots were 14,460 at US $0.19 per ThruPlay. Ad spots related to home safety had a significantly higher daily ThruPlay rate than those related to fire safety (6.5 vs 0.5 per day; P<.001). CONCLUSIONS: Social media is a feasible modality for delivering public health messages focused on preventing pediatric burn injuries. Engagement with these ads is influenced by ad presentation and the focus of the underlying injury prevention message.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".