Prevalence of the Patterns of Unhealthy Diet in the School and University Students of Iran: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Introduction. The present study was conducted to investigate the pooled prevalence rate of the different patterns of unhealthy diet among the school and university students of Iran. Methods. In this systematic review, the type of the main question was regarding prevalence and the effect measure was prevalence rate reported along with 95% confidence interval (CI). Data bases including PubMed, Scopus, and Web of Science as well as Google Scholar and Persian resources were used. The Newcastle–Ottawa scale (NOS) checklist was used for quality assessment of studies. Results. The extracted types of unhealthy diet in the present systematic review were “breakfast skipper,” “fast food,” “hydrogenated oils consumption,” “salty snacks,” “sweetened beverages,” “breakfast skipper,” “dinner skipper,” “launch skipper,” and “sweets.” The range of pooled prevalence for different types was 0.06–0.75. The data of 16,321 subjects included in six studies were analyzed. The pooled prevalence of unhealthy diet was 0.28 (95% CI: 0.23–0.33, I2 > 99%) overall, 0.25 (95% CI: 0.20–0.31, I2 > 99%) in school students and 0.37 (95% CI: 0.12–0.62, I2 > 99%) in university students. The most prevalent pattern was breakfast skipping 0.39 (95% CI: 0.28–0.50) followed by consumption of sweetened beverages 0.31 (95% CI: 0.20–0.43). The pooled prevalence range among the patterns was 0.06–0.75 (random effects for all). Conclusion. The pooled prevalence was 28% for unhealthy diet among the Iranian students (6% to 75% in different patterns). Although there was uncertainty regarding the pooled evidence, the whole of the mentioned range was clinically important for health policymakers. Decisions should be made on the basis of the patterns.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".