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Record W2151847432 · doi:10.1123/jpah.6.6.708

Children's Organized Physical Activity Patterns From Childhood into Adolescence

2009· article· en· W2151847432 on OpenAlexaffabout
Leanne Findlay, Rochelle Garner, Dafna Kohen

Bibliographic record

VenueJournal of Physical Activity and Health · 2009
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsLongitudinal studyPhysical activityDemographyPsychologyLongitudinal dataDevelopmental psychologyLow incomeEarly childhoodGerontologyMedicinePhysical therapySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Few longitudinal studies of physical activity have included young children or used nationally representative datasets. The purpose of the current study was to explore patterns of organized physical activity for Canadian children aged 4 through 17 years. METHODS: Data from 5 cycles of the National Longitudinal Survey of Children and Youth were analyzed separately for boys (n = 4463) and girls (n = 4354) using multiple trajectory modeling. RESULTS: Boys' and girls' organized physical activity was best represented by 3 trajectory groups. For boys, these groups were labeled: high stable, high decreasing, and low decreasing participation. For girls, these groups were labeled: high decreasing, moderate stable, and low decreasing participation. Risk factors (parental education, household income, urban/rural dwelling, and single/dual parent) were explored. For boys and girls, having a parent with postsecondary education and living in a higher income household were associated with a greater likelihood of weekly participation in organized physical activity. Living in an urban area was also significantly associated with a greater likelihood of weekly participation for girls. CONCLUSIONS: Results suggest that Canadian children's organized physical activity is best represented by multiple patterns of participation that tend to peak in middle childhood and decline into adolescence.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.015
GPT teacher head0.310
Teacher spread0.294 · 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.

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

Citations75
Published2009
Admission routes2
Has abstractyes

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