Practice what you preach': Induced hypocrisy as an intervention strategy to reduce children's intentions to risk take on playgrounds
Bibliographic record
Abstract
Objective An intervention based on induced hypocrisy was conducted to reduce children’s intentions to show fall-risk behaviors on playground equipment. Methods To induce hypocrisy participants (7–12 years) advocated for safe-play behaviors while being made mindful about past failures to play safely on playgrounds. To measure the impact of the intervention, children created posters indicating which playground behaviors they Would Do and Would Not Do; children believed they would have to demonstrate the behaviors endorsed on their posters. Results The intervention resulted in significant reductions in the risk behaviors children endorsed both for risk behaviors targeted (specific effects) and nontargeted risk behaviors (generalized effects). Assessing the mechanism by which the intervention produced changes in behavioral intentions revealed it yielded changes in cognitions that predicted subsequent reductions in risk-taking intentions. Conclusions The present intervention was successful in reducing children’s intentions to engage in risky playground behaviors and it did so by changing cognitions. Key words children; fall prevention; playground injury; program evaluation; risk behaviors. Injuries on Playgrounds Unintentional injuries are the leading cause of death for North American youth 1–18 years of age (Canadian
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".