121 Using peer communicated behavioural norms about safety to reduce injury-risk behaviours by children
Bibliographic record
Abstract
Background Previous research has shown that children engage in greater physical risk taking when in an elevated positive mood state. The current study examined whether exposure to a peer-communicated behavioural norm about safety could counteract this effect. Methods Community recruitment resulted in a sample of 120 children (7 to 10 years), including 60 boys (M age = 8.13 yrs; SD = 0.93 yrs) and 60 girls (M age = 8.02 yrs; SD = 0.91 years). Children’s intentions to engage in risk taking (based on identifying from photos which risky playground behaviours they would do if they had to make a videotape later that day) and actual risk behaviours (based on how they behaved when running through an obstacle course that contained hazards) were measured while in a neutral and positive mood state, with positive mood induced experimentally via false positive feedback during the playing of a novel videogame (emotion ratings throughout the session validated the positive mood induction worked; there was a significant increase in positive mood, as expected, t(119) = 15.12, p < .001). Before completing the risk taking tasks when in a positive mood state, children were exposed to either a peer-communicated behavioural norm about safety or a non-norm communication; this exposure occurred by the child overhearing two children supposedly talking next door (this was actually an audiotaped recording). Results Exposure to the non-norm communication had no effect on risk taking: children showed an increase in risk taking and intentions when in a positive aroused mood state compared to a neutral mood state (M change = +0.65 standardised RT score), F(1, 59) = 71.31, p < .001, ηp2 = 0.55. In contrast, exposure to the peer-communicated behavioural norm about safety was effective to counteract this effect: children actually showed a significant decrease in risk taking and intentions when in a positive compared to neutral mood state (M change = −0.47 standardised RT score), F(1, 115) = 84.77, p < .001, effect size ηp2 = 0.42. Both risk taking measures yielded the same effects. Conclusion Manipulating children’s exposure to peer-communicated behavioural norms can be an effective strategy for reducing injury-risk behaviours.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".