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Record W3088466541 · doi:10.1080/02640414.2020.1822585

Socioeconomic and gender-based disparities in the motor competence of school-age children

2020· article· en· W3088466541 on OpenAlexafffund
Véronique Gosselin, Mario Leone, Suzanne Laberge

Bibliographic record

VenueJournal of Sports Sciences · 2020
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversité du Québec à ChicoutimiUniversité de Montréal
FundersFonds de Recherche du Québec-Société et Culture
KeywordsSocioeconomic statusOddsPsychologyCompetence (human resources)Developmental psychologyPsychological interventionOdds ratioLogistic regressionDemographyMedicineSocial psychologyPopulationSociology

Abstract

fetched live from OpenAlex

This study examined socioeconomic and gender-based disparities in motor competence (MC) amongst 6-12-year-old children (N = 2654). Validated product-oriented tests assessing agility, balance and coordination were used to measure MC. School-level socioeconomic status (low, middle, high) was used to assess socioeconomic disparities. Analysis of covariance (ANCOVA) were conducted and odds ratios were calculated for the likelihood of having low MC by gender and socioeconomic status (SES). Girls displayed lower MC than boys for agility and coordination involving object-control (P < 0.001) while boys scored lower than girls for balance and hand-foot coordination (P < 0.001). Children in high SES schools displayed the highest level of MC for agility, balance and coordination (P < 0.001). Compared to the children in high SES schools, odds of having low competence in balance was higher for the children in low SES schools and odds of having low competence in agility and coordination were higher for the children in both low and middle SES schools. Newell’s model of constraints (1986) and Bourdieu’s concept of habitus (1984) were used to consider potential explanations of the observed disparities. To level up inequalities in children’s MC, resources invested in school-based interventions should be proportionate to the school SES.

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)0.000
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 score0.003
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.039
GPT teacher head0.284
Teacher spread0.244 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

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