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Record W4389640330 · doi:10.1136/ip-2023-045017

Adolescents at the skatepark: identifying design features and youth behaviours that pose risk for falls

2023· article· en· W4389640330 on OpenAlexafffund
Barbara A. Morrongiello, Maria Amir, Michael R. Corbett, Caroline Zolis, Kelly Russell

Bibliographic record

VenueInjury Prevention · 2023
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of ManitobaUniversity of Guelph
FundersCanadian Institutes of Health Research
KeywordsFalling (accident)Injury preventionSuicide preventionHuman factors and ergonomicsPoison controlOccupational safety and healthPsychologyMedicineApplied psychologyEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Skateboarding is an increasingly popular leisure activity for youth, yet injuries due to falls are common. This study aimed to identify the features at skateparks and tricks performed by youth that pose an increased risk of falls in skateboarders. METHOD: Video recordings were unobtrusively taken at a large skatepark of youth designated as young (11-15 years) or old (16-20 years). Videos were coded to identify the popular skatepark features used and tricks performed, and to assign a fall severity outcome rating for each feature and each type of trick attempted. RESULTS: The results identify features and tricks that pose increased risk of falling for youth at skateparks. CONCLUSIONS: Implications for injury prevention are discussed, including a consideration of environmental (skatepark design) and individual (youth behaviour) factors relevant to reducing skateboarding injuries due to falls among youth.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.241
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.079
GPT teacher head0.374
Teacher spread0.295 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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