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Record W4396615623 · doi:10.1123/jsep.2023-0168

The Sport Experience Measure for Children and Youth (SEM:CY): A Rasch Validation Study

2024· article· en· W4396615623 on OpenAlexaffabout
Philip Jefferies, Matthew Kwan, Denver M. Y. Brown, Mark W. Bruner, Katherine A. Tamminen, John Cairney

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of TorontoNipissing UniversityBrock UniversityDalhousie University
Fundersnot available
KeywordsRasch modelPsychologyPolytomous Rasch modelMeasure (data warehouse)Developmental psychologyApplied psychologySocial psychologyPsychometricsItem response theoryData mining

Abstract

fetched live from OpenAlex

This study employed Rasch analyses to validate a novel measure of sport experience: the Sport Experience Measure: Children and Youth (SEM:CY). Analyses were applied to self-reported data of n = 503 young people (age 9-18 years, M = 12.91, 50% female) in Canada who were engaging in sport during the previous 12 months. The revised measure, consisting of 24 items on a 3-point response scale, demonstrated good fit statistics (e.g., item fit residual: M = -0.50, SD = 0.94 and person fit residual: M = -0.62, SD = 2.33), an ability to reliably discriminate between levels of sport experience, and an absence of differential item functioning for various groups (males and females, older and younger individuals, solo and team sports, and those playing at various competitive levels, including recreation). The SEM:CY is a succinct tool that can serve as a valuable means to gauge the quality of an individual's sport experience, which can facilitate positive youth development and sustain participation across the life span.

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)0.000
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 score0.130
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.355
Teacher spread0.324 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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