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Record W2552028422 · doi:10.1123/jpah.2016-0404

Results From Malaysia’s 2016 Report Card on Physical Activity for Children and Adolescents

2016· article· en· W2552028422 on OpenAlexaboutno aff
Razinah Sharif, Kar Hau Chong, Nur Hadiyani Zakaria, Min Li Ong, John J. Reilly, Jyh Eiin Wong, Hazizi Abu Saad, Bee Koon Poh

Bibliographic record

VenueJournal of Physical Activity and Health · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsReport cardPhysical activityGrading (engineering)Sedentary behaviorMedicineProtocol (science)Physical therapyPsychologyEnvironmental healthEngineeringAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The 2016 Malaysia Active Healthy Kids Report Card aims to collect, assess, and grade current and comprehensive data on physical activity (PA) and associated factors in Malaysian children and adolescents aged 5 to 17 years. METHODS: This report card was developed following the Active Healthy Kids Canada Report Card protocol. The Research Working Group identified the core matrices, assessed the key data sources, and evaluated the evidence gathered for grade assignments. A grade was assigned to each indicator by comparing the best available evidence against relevant benchmark using a standardized grading scheme. RESULTS: Overall Physical Activity, Active Transportation, and Sedentary Behavior were assigned the D grade. The lowest grade of F was assigned to Diet, while School and Government Strategies and Investments were graded higher with a B. Five indicators were assigned INC (incomplete) due to a lack of representative data. CONCLUSIONS: The report card demonstrates that Malaysian children and adolescents are engaging in low levels of PA and active commuting, high levels of screen time, and have extremely low compliance with dietary recommendations. More efforts are needed to address the root causes of physical inactivity while increasing the opportunities for children and adolescents to be more physically active.

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 score0.865
Threshold uncertainty score0.841

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 integrity0.0000.001
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 head0.031
GPT teacher head0.337
Teacher spread0.306 · 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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