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Record W7032835600

Perception towards child obesity: a study in Ranau District Office / Aizatul Fatin Japinin and Noor Farrah Azreena Ahmad Murad

2016· other· en· W7032835600 on OpenAlexaboutno aff

Bibliographic record

VenueUiTM Institutional Repositories (Universiti Teknologi MARA) · 2016
Typeother
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsnot available
Fundersnot available
KeywordsObesityOverweightChristian ministryQuarter (Canadian coin)Childhood obesityPublic health
DOInot available

Abstract

fetched live from OpenAlex

Malaysia already holds the dubious award for the highest rate of obesity in the continent of Asia, according to British medical journal The Lancet. Of note is that the Malaysian Association for the Study of Obesity (MASO) had already published a paper in 2005 that would show the trend of obesity in Malaysia reaching where it is now. Malaysia is ranked sixth in the Asia-Pacific region for obesity and tops the list in South-East Asia for both obesity and diabetes. Nearly 45% of Malaysian men and almost half of women are overweight or obese. Obesity is a condition that related to having an excess of body fat. Obesity is classified as having a body max index (BMI) of 30 or greater. BMI is a tool used to measure obesity. The situation become more critical, because children are listed to be among those who has contributed to increase this problem. The World Health Organization (WHO) regards childhood obesity as one of the most serious global public health challenges for the 21st century. Dr. Alya Hamzah from The Malaysian Medical Gazette have made estimation of every five school going Malaysian children is obese or overweight. Other than that, In April 2012, a survey by the Ministry of Health revealed that just over a quarter of Malaysian school children were obese or overweight. This indicates that, how serious the problem of obesity among children in Malaysia.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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