87 Using peer communicated norms about safety to reduce injury-risk behaviours by children
Bibliographic record
Abstract
Purpose This intervention study examined whether exposure to a peer safety norm could counteract the increase in risk taking children show when in an elevated positive mood state. Methods Risk taking (intentions, behaviours) was measured in a neutral and positive (induced experimentally) mood state. Before completing the tasks in a positive mood, 120 children 7–10 years were exposed to either a safety norm or a control audio. Results The control audio had no effect on risk taking: children showed an increase in risk taking and intentions when in a positive compared to neutral mood, replicating past research. In contrast, exposure to the safety norm counteracted this effect: children actually showed a decrease in risk taking and intentions when in a positive compared to neutral mood. Conclusion Manipulating children’s exposure to social norms can be an effective strategy for reducing injury-risk behaviours even when they are in an elevated positive mood state. Significance This study is an important step towards identifying strategies to reduce risky play behaviours that can lead to injury in school-aged children. The influence of other children, as well as elevated mood, have both been identified as substantial risk factors for injury during the school years, so examining how peer relationships can help to mitigate the increase in risk taking when children are in a positive mood is important. The manipulation of children’s perceptions of peer social norms is a prevention strategy that can be applied across a broad age range and to numerous situations where risk behaviours are likely to lead to injury (e.g., sports). The current findings, therefore, have important implications for preventing childhood injuries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".