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Record W2549353319 · doi:10.1123/jpah.2016-0308

Results From Spain’s 2016 Report Card on Physical Activity for Children and Youth

2016· article· en· W2549353319 on OpenAlexaboutno aff
Blanca Román‐Viñas, Jorge Marín-Puyalto, Mairena Sánchez‐López, Susana Aznar, Rosaura Leis, Raquel Aparicio‐Ugarriza, Helmut Schröder, Rocío Ortiz-Moncada, Germán Vicente, Marcela González‐Gross, Lluís Serra‐Majem

Bibliographic record

VenueJournal of Physical Activity and Health · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsReport cardPhysical activitySedentary behaviorGovernment (linguistics)PsychologyActive livingMedicineMedical educationPhysical therapyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: The first Active Healthy Kids Spanish Report Card aims to gather the most robust information about physical activity (PA) and sedentary behavior of children and adolescents. METHODS: A Research Working Group of experts on PA and sport sciences was convened. A comprehensive data search, based on a review of the literature, dissertations, gray literature, and experts' nonpublished data, was conducted to identify the best sources to grade each indicator following the procedures and methodology outlined by the Active Healthy Kids Canada Report Card model. RESULTS: Overall PA (based on objective and self-reported methods) was graded as D-, Organized Sports Participation as B, Active Play as C+, Active Transportation as C, Sedentary Behavior as D, School as C, and Family and Peers as Incomplete, Community and the Built Environment as Incomplete, and Government as Incomplete. CONCLUSIONS: Spanish children and adolescents showed low levels of adherence to PA and sedentary behavior guidelines, especially females and adolescents. There is a need to achieve consensus and harmonize methods to evaluate PA and sedentary behavior to monitor changes over time and to evaluate the effectiveness of policies to promote PA.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.340
Teacher spread0.297 · 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

Citations36
Published2016
Admission routes1
Has abstractyes

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