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Record W4410825364 · doi:10.3390/sports13060165

Relationship Between Socioeconomic Status and Organized Sports Among Primary School Children: A Gender-Based Analysis of Sports Participation

2025· article· en· W4410825364 on OpenAlexaff
Chiaki Tanaka, Eun‐Young Lee, Shigeho Tanaka

Bibliographic record

VenueSports · 2025
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsQueen's University
Fundersnot available
KeywordsSocioeconomic statusPsychologyDevelopmental psychologyEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Sports participation according to socioeconomic status (SES) was related to children in high-income Western countries. This study aimed to examine whether family or neighborhood-level SES is associated with current and continued organized sports participation, including the types of sports, among Japanese primary school children from preschool onward. The participants consisted of 269 girls, 255 boys, and their parents. Data on the type of sports participation at the current school or preschool, parental employment, and education were collected by questionnaire. Neighborhood-level SES was evaluated by the average annual income within 4 km of each school. The odds of sports participation was higher among children with mothers identifying as housewives or those with mothers employed part-time. Among girls, the odds of continued sports participation were lower if their mothers were junior high school or high school graduates or junior college/vocational school graduates. The odds of sports type like swimming were higher for children whose mothers had part-time jobs. Lower average community income was associated with lower participation in football and higher participation in baseball. These findings suggest that mothers' employment and academic background are important correlates of sports participation for children, with variations observed by sport type and gender.

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)0.001
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 score0.005
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.315
Teacher spread0.296 · 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
Published2025
Admission routes1
Has abstractyes

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