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Record W2550432729 · doi:10.1123/jpah.2016-0403

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

2016· article· en· W2550432729 on OpenAlexaboutno aff
Lisbeth Runge Larsen, Jens Troelsen, Kasper Lund Kirkegaard, Søren Riiskjær, Rikke Fredenslund Krølner, Lars Østergaard, Peter Lund Kristensen, Niels Christian Møller, Björn Christensen, Jens-Ole Jensen, Charlotte Østergård, Thomas Skovgaard

Bibliographic record

VenueJournal of Physical Activity and Health · 2016
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsReport cardPhysical activityPsychologyGerontologyMedicinePhysical therapyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: The first Danish Report Card on Physical Activity (PA) for Children and Youth describes Denmark's efforts in promoting and facilitating PA and PA opportunities for children and youth. METHODS: The report card relies primarily on a synthesis of the best available research and policy strategies identified by the Report Card Research Committee consisting of a wide presentation of researchers and experts within PA health behaviors and policy development. The work was coordinated by Research and Innovation Centre for Human Movement and Learning situated at the University of Southern Denmark and the University College Lillebaelt. Nine PA indicators were graded using the Active Healthy Kids Canada Report Card development process. RESULTS: Grades from A (highest) to F (lowest) varied in Denmark as follows: 1) Overall Physical Activity (D+), 2) Organized Sport Participation (A), 3) Active Play (INC; incomplete), 4) Active Transportation (B), 5) Sedentary Behaviors (INC), 6) Family and Peers (INC), 7) School (B), 8) Community and the Built Environment (B+), and 9) Government strategies and investments (A-). CONCLUSIONS: A large proportion of children in Denmark do not meet the recommendations for PA despite the favorable investments and intensions from the government to create good facilities and 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.641

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 studies0.0000.000
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 head0.042
GPT teacher head0.344
Teacher spread0.302 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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