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Record W929591471 · doi:10.1007/s12160-015-9721-4

Predicting Changes Across 12 Months in Three Types of Parental Support Behaviors and Mothers’ Perceptions of Child Physical Activity

2015· article· en· W929591471 on OpenAlexafffundabout
Ryan E. Rhodes, John C. Spence, Tanya R. Berry, Sameer Deshpande, Guy Faulkner, Amy E. Latimer‐Cheung, Norm O’Reilly, Mark S. Tremblay

Bibliographic record

VenueAnnals of Behavioral Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsQueen's UniversityUniversity of TorontoUniversity of LethbridgeUniversity of AlbertaChildren's Hospital of Eastern OntarioUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsHealth psychologyPsychologyDevelopmental psychologyPhysical activityPerceptionClinical psychologyMedicinePublic healthPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Parental support has been established as the critical family-level variable linked to child physical activity with encouragement, logistical support, and parent-child co-activity as key support behaviors. PURPOSE: This study aims to model these parental support behaviors as well as family demographics as mediators of mothers' perceptions of child physical activity using theory of planned behavior (TPB) across two 6-month waves of longitudinal data. METHOD: A representative sample of Canadian mothers (N = 1253) with children aged 5 to 13 years of age completed measures of TPB, support behaviors, and child physical activity. RESULTS: Autoregressive structural equation models showed that intention and perceived behavioral control explained support behaviors, yet child age (inverse relationship) and family income were independent predictors. The three support behaviors explained 19-42 % of the variance in child physical activity between participants, but analyses of change showed much smaller effects. CONCLUSIONS: Mothers' support behaviors are related to perceived child physical activity, but support is dependent on perception of control, child age, and family income.

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 score0.047
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.179
GPT teacher head0.445
Teacher spread0.267 · 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

Citations38
Published2015
Admission routes3
Has abstractyes

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