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Record W2412071943 · doi:10.1123/jpah.2016-0012

Are the Physical Activity Parenting Practices Reported by US and Canadian Parents Captured in Currently Published Instruments?

2016· review· en· W2412071943 on OpenAlexfundaboutno aff
Louise C. Mâsse, Teresia M. O’Connor, Andrew W. Tu, Allison W. Watts, Mark R. Beauchamp, Sheryl O. Hughes, Tom Baranowski

Bibliographic record

VenueJournal of Physical Activity and Health · 2016
Typereview
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsPhysical activityPsychologyDevelopmental psychologyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to compare the physical activity parenting practices (PAPPs) parents report using with the PAPPs incorporated in the published literature. METHODS: PAPPs in the literature were identified by reviewing the content of 74 published PAPP measures obtained from current systematic reviews supplemented with a literature search. The types of PAPPs used by parents were identified by surveying a stratified sample of 134 Canadian and US parents of 5- to 12 year-old children. Items from the literature and parent responses were coded using the same coding scheme. Differences between the PAPPs emphasized by the parents and the literature were examined. RESULTS: Parents significantly emphasized different issues than what is measured in the literature (P < .001). Parents emphasized more control (13.6% vs. 6.9%), modeling and teaching (13.2% vs. 9.2%), and structural strategies (32.2% vs. 28.6%) and less autonomy support (11.8% vs. 14.0%), logistical support (9.9% vs. 12.8%), and responsiveness strategies (19.3% vs. 28.5%). CONCLUSIONS: Physical activity practices most often employed by parents are not the ones emphasized in current measures. The extent to which putting more emphasis on the areas identified by parents will increase the predictive validity of the measures warrants further examination.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
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 head0.097
GPT teacher head0.411
Teacher spread0.314 · 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 designOther design
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

Citations21
Published2016
Admission routes2
Has abstractyes

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