Study on the Effect of Consumption of Sugar-Sweetened Beverages on Psychosocial Behavior of Children in Saudi Arabia
Bibliographic record
Abstract
Background and objectives: The goal of the study was to see how sugar-enhanced beverages affected children's psychosocial behavior. Expansions in the use of sugar-enhanced drinks (SSB) during youth have mirrored global patterns in the epidemic of experience growing up stoutness. Furthermore, the study aims to determine the rates at which Saudi children use SSBs and their relationship to mental effects, as well as the mental aspects that are most affected by SSB use. Methodology: For this research, 400 guardians from Saudi Arabia were selected as samples, and the probability purposive sampling technique was used to collect samples. Questionnaires were designed and validated through the pilot survey. Three categories were made in the questionnaires to assess the sociodemographic characteristics, consumption patterns of sugar-sweetened beverages, and behavioral problems. Chi-square, t-test, and logistic regression were used to analyze the data statistically using SPSS 23 software. Results: Results and outcomes of the research demonstrated that mental health issues and physical as well as psychosocial problems were the main effects of excessive use of sugar-sweetened beverages Causes. Restlessness, lack of concentration, loss of temper, lack of confidence, and feelings of sickness were found to be the most experienced symptoms. Conclusion: The intake of sugar-sweetened beverages negatively impacts children's physical and mental health. It impacts increased heart issues, obesity, diabetes, and aggressive behavior. Limitations: This is a cross-sectional study, and the causal relationship is unclear.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".