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Record W2052405934 · doi:10.1037/a0023857

The impact of accumulated experience on children's appraisals of risk and risk-taking decisions: Implications for youth injury prevention.

2011· article· en· W2052405934 on OpenAlexafffund
Jennifer Lasenby-Lessard, Barbara A. Morrongiello, Deb Barrie

Bibliographic record

VenueHealth Psychology · 2011
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health ResearchOntario Neurotrauma Foundation
KeywordsPsychologyRecreationSensation seekingSuicide preventionInjury preventionVulnerability (computing)Human factors and ergonomicsSet (abstract data type)Risk perceptionPoison controlDevelopmental psychologyRisk assessmentSocial psychologyEnvironmental healthMedicinePerceptionPersonalityComputer security

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.191
GPT teacher head0.539
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2011
Admission routes2
Has abstractyes

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