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Record W2555206421 · doi:10.1123/jpah.2016-0307

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

2016· article· en· W2555206421 on OpenAlexaboutno aff
Christine Delisle Nyström, Christel Larsson, Bettina Ehrenblad, Hanna Eneroth, Ulf Eriksson, Marita Friberg, María Hagströmer, Anna Karin Lindroos, John J. Reilly, Marie Löf

Bibliographic record

VenueJournal of Physical Activity and Health · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsReport cardPhysical activityHealth promotionChildhood obesitySedentary behaviorPsychologyPromotion (chess)ObesityGovernment (linguistics)Screen timeGerontologyMedicinePublic healthPhysical therapyOverweightPolitical scienceNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The 2016 Swedish Report Card on Physical Activity (PA) for Children and Youth is a unique compilation of the existing physical and health related data in Sweden. The aim of this article is to summarize the procedure and results from the report card. METHODS: Nationally representative surveys and individual studies published between 2005-2015 were included. Eleven PA and health indicators were graded using the Active Healthy Kids Canada grading system. Grades were assigned based on the percentage of children/youth meeting a defined benchmark (A: 81% to 100%, B: 61% to 80%, C: 41% to 60%, D: 21% to 40%, F: 0% to 20%, or incomplete (INC). RESULTS: The assigned grades were Overall Physical Activity, D; Organized Sport Participation, B+; Active Play, INC; Active Transportation, C+; Sedentary Behaviors, C; Family and Peers, INC; School, C+; Community and the Built Environment, B; Government Strategies and Investments, B; Diet, C-; and Obesity, D. CONCLUSIONS: The included data provides some support that overall PA is too low and sedentary behavior is too high for almost all age groups in Sweden, even with the many national policies as well as an environment that is favorable to the promotion of 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 score0.920
Threshold uncertainty score0.825

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 communication0.0000.001
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 head0.049
GPT teacher head0.350
Teacher spread0.301 · 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

Citations40
Published2016
Admission routes1
Has abstractyes

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