119 Does exposure to general warnings in framed messages reduce risk behaviours in school-aged children?
Bibliographic record
Abstract
<h3>Statement of Purpose</h3> Framed safety messages (gain- or loss- framed) can counteract the increase in risk taking that occurs when children are in a heightened positive mood. In previous research, framed safety messages have consisted of behaviourally targeted messages that emphasize avoiding risk behaviors leading to specific injuries and outcomes. The current study examined whether more general warning messages in framed contexts had a differential effect on reducing risk taking in children when in a heightened positive mood. <h3>Methods/Approach</h3> 39 children (7–9 years) were exposed to a safety message (gain- or loss-frame) regarding play behaviors on an obstacle course. Children’s risk-taking running the obstacle course was measured before and after a positive mood induction. <h3>Results</h3> Participants who were exposed to loss-framed safety messages in both the general and behaviorally targeted groups demonstrated a significantly lower level of risk taking compared to baseline, whereas participants who were exposed to gain-framed safety messages in both groups performed at baseline levels. Regardless of whether children were exposed to general or behaviorally specific messages, gain and loss messaging counteracted the increase in risk taking when in a positive mood, but loss messages produced greater reductions in risk taking than gain messages. <h3>Conclusion</h3> The results indicate that general messages can be as effective as behaviorally specific messages. Moreover, the loss-framed safety message had a greater effect on reducing risk-taking in children when in a heightened positive mood than the gain-framed safety message. <h3>Significance and Contributions</h3> The results suggest that placing an emphasis on specific risk-taking behaviors and outcomes is not necessary in order to reduce risk-taking behaviours in school-aged children during play situations. This makes this intervention approach feasible to apply in situations in which there are a variety of potential risk behaviors which makes targeting a specific one likely to limit effectiveness of the intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".