Does Exposure to General Warnings in Framed Messages Reduce Risk Behaviors in School-Aged Children?
Bibliographic record
Abstract
Children in a heightened positive mood state engage in more risk-taking behaviors (Morrongiello et al., 2017; Morrongiello et al., 2014; Seasons, 2018). Framed safety messages (gain- or loss- framed) can counteract this increase in risk taking that occurs when children are in a heightened positive mood (Seasons, 2018). In previous research, framed safety messages have consisted of behaviourally targeted messages that place an emphasis on avoiding risk behaviors leading to specific injuries and outcomes. The current study examined whether delivering more general warning messages in framed contexts had a differential effect on reducing risk taking in children when in a heightened positive mood. 26 children (aged 7-9 years old) were exposed to a general safety message (gain-frame, loss-frame, or control message) regarding play behaviors on an obstacle course (risk taking measure). Children’s risk-taking running the obstacle course was measured before and after a positive mood induction. Results indicated that the mood induction was successful and led to increased risk-taking. Gain-framed and loss-framed safety messages both counteracted this increase in risk-taking, but loss-framed messages yielded larger reductions. There was no differential effect based on exposure to general versus behaviorally targeted framed safety messages. Implications for injury prevention are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".