Study on Relationship of Psychopathology on Childhood Obesity Among School Children in South India
Bibliographic record
Abstract
Study on relationship of psychopathology on childhood obesity among school children in South India. Aim: To study the relationship of psychopathology on obesity among school children of Davangere district, South India. Settings and Design: School children studying in V-VII standard of Davangere dist in Karnataka, south India. Stage-I cross sectional study for identification of obese children. Stage-II Case control study to find out the relationship of psychopathology and obesity among school children. Material and Methods: Body mass index (BMI) was calculated using BMI charts based on NCHS standards. 421 obese children and 842 controls (1:2 ratios) were studied by using Childhood Psychopathology Measurement Schedule (CPMS) tool developed and standardized by Malhotra et al. Statistical Analysis: Data analysis was conducted at SPSS/ PC programme (Version 13). Results and Conclusions: The psychopathology was present in 44.2% of obese children by using CPMS tool as against only 13.8% of non obese children. The presence of psychopathology among obese boys was more (59.5%) than obese girls (49.4%). The obese children had the relationship of psychopathology 4.9 times more than nonobese children. The positive association of psychopathology with obesity was present in both sexes. The association was found to be stronger for boys in comparison to girls. Our study findings show that psychopathology has strong relationship on obesity among school children and psychopathology has a definite impact on childhood obesity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".