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Record W4376105814 · doi:10.1080/1091367x.2023.2211980

Concurrent and Convergent Validity of the Child Focused Injury Risk Screening Tool (ChildFirst) for 8-12-Year-Old Children

2023· article· en· W4376105814 on OpenAlexaff
John A. Jimenez-Garcia, Chanelle Montpetit, Richard DeMont

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2023
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsConcordia University
Fundersnot available
KeywordsConcurrent validityConvergent validityPsychologyFace validityMovement assessmentCorrelationGross motor skillPredictive validityPhysical medicine and rehabilitationPhysical therapySagittal planeBalance testMotor skillDevelopmental psychologyPsychometricsBalance (ability)MedicineInternal consistencyMathematics

Abstract

fetched live from OpenAlex

The Child Focused Injury Risk Screening Tool (ChildFIRST) aims to measure movement competence and lower-limb-injury risk in 8–12-year-old children. Although the ChildFIRST has face and content validity evidence, stronger validity evidence is warranted. We tested the concurrent validity of the ChildFIRST using motion analysis, and the convergent validity of the ChildFIRST using the modified Star Excursion Balance Test (mSEBT) and the Test of Gross Motor Development 3 (TGMD-3). We computed correlation coefficients (0.05 alpha level). We evaluated 17 participants. We observed positive correlation values between 18 ChildFIRST evaluation criteria and peak joint angles in the frontal and sagittal planes. One movement skill (i.e. leaping) presented a negative correlation value. We observed positive correlation values between the ChildFIRST and TGMD-3 and between the ChildFIRST and the mSEBT. Nine out of ten movement skills in the ChildFIRST are valid to assess movement competence and identify risk factors associated with lower-limb musculoskeletal injuries.

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)0.001
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 score0.135
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.054
GPT teacher head0.333
Teacher spread0.279 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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