The impact of accumulated experience on children's appraisals of risk and risk-taking decisions: Implications for youth injury prevention.
Bibliographic record
Abstract
OBJECTIVES: This study assessed whether repeated experience with a physical activity leads to increased risk taking and compared what factors (risk appraisals, emotion ratings, child attributes) predict risk taking before and after practice doing the activity. METHOD: Children 7 to 12 years of age participated in an ecologically valid risk-taking task in which they chose the highest height at which to set a balance beam before and after they practiced walking across it. RESULTS: Prior to accumulating experience, predictors of risk taking included appraisals of risk, child attributes, and extent of past experience with the activity. After accumulating experience, risk taking increased and was predicted by behavioral attributes (low inhibitory control, high sensation seeking) and appraisal of perceived vulnerability. CONCLUSION: When aiming to reduce risk taking, the best approach will be one that targets different determinants depending on children's extent of experience with the recreational activity.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".