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Record W2551101139 · doi:10.1123/jpah.2016-0296

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

2016· article· en· W2551101139 on OpenAlexaboutno aff
Chiaki Tanaka, Shigeho Tanaka, Shigeru Inoue, Motohiko Miyachi, Koya Suzuki, John J. Reilly

Bibliographic record

VenueJournal of Physical Activity and Health · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsReport cardRecreationPhysical activityContext (archaeology)Sedentary behaviorPsychologyPhysical fitnessGovernment (linguistics)Screen timeGerontologyEnvironmental healthApplied psychologyMedicinePhysical therapyPolitical scienceGeography

Abstract

fetched live from OpenAlex

BACKGROUND: The Report Card on Physical Activity for Children and Youth aims to consolidate existing evidence, encourage greater evidence-informed physical activity, and improve surveillance of physical activity. METHODS: The Japan report card followed the methodology of the Canadian and Scottish report cards, but was adapted to reflect the Japanese context. Nationally representative data were used to score each of the respective indicators. RESULTS: The 2016 Japan Report Card on Physical Activity for Children and Youth consists of Health Behaviors and Outcomes (7 indicators), and Influences on Health Behaviors (4 indicators). Three Health Behaviors and Outcomes received C grades (Participation in Sport; Sedentary Behavior; Recreational Screen Time; Physical Fitness), while 2 indicators could not be graded (Overall Physical Activity, and Active Play). The indicators Active Transportation (B) and Weight Status were favorable (A). In the Influences domain, Family Influence and Community and the Built Environment were graded as D, while School and Government Strategies and Investments were favorable (B). CONCLUSIONS: The Japan report card illustrated some favorable health behaviors, health outcomes, and influences. There is a need for more evidence especially on overall physical activity levels, active play, and community and the built environment.

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 score0.925
Threshold uncertainty score0.826

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 head0.043
GPT teacher head0.341
Teacher spread0.298 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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