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Record W4385507981 · doi:10.1080/14927713.2023.2242859

Children’s knowledge about play-related risk, risk-taking, and injury: A meta-study

2023· article· en· W4385507981 on OpenAlexaffvenue
Kyle McCallum, Jessica Youngblood, Alix Hayden, Mariana Brussoni, Carolyn A. Emery, William Bridel

Bibliographic record

VenueLeisure/Loisir · 2023
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsBC Children's HospitalSpinal Cord Injury BCUniversity of British Columbia HospitalLearning PartnershipUniversity of Calgary
Fundersnot available
KeywordsMeta-analysisMedicinePsychologyInternal medicine

Abstract

fetched live from OpenAlex

Risk-taking in play has received increased focus in research over the last three decades through the use of quantitative, qualitative, and mixed methods. The purpose of this meta-study was to review the qualitative literature specifically to better understand how children (5–16 years of age) understand the concepts of risk, risky play, and injury in relation to their play experiences. Twenty-two studies were identified for inclusion in the meta-study. Children across studies demonstrated nuanced understandings of risk and risk-taking, identified specific mediators related to their risky play endeavours, and discussed the presence and acceptance of injury in their play pursuits. Children’s knowledge and perceptions often paralleled adult understandings of risk and risky play, but also highlighted children’s risk identification and management abilities. The findings of the meta-study suggest a still greater need for understanding children’s knowledge about risk in relation to play, such as their understandings of safety and how autonomy in risk-taking may augment their physical and cognitive development.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.036
GPT teacher head0.345
Teacher spread0.309 · 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.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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