Perception towards child obesity: a study in Ranau District Office / Aizatul Fatin Japinin and Noor Farrah Azreena Ahmad Murad
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".