The Prevalence of Enuresis and Its Association with Psychological Factors in Zahedan, a City of Iran
Bibliographic record
Abstract
INTRODUCTION: Enuresis is a common problem with multiple causes in children. The aim of the present study was to establish the prevalence of enuresis among school population and its psychology and emotional associated factors. METHODS: Clustering sampling was used to collect 2000 schoolchildren from city Zahedan. A research made questionnaire was applied as a tool for data collection. All parents were informed about the aims of study and signed the consent form. Statistical analyses were performed using SPSS 19.0. Odds ratio and χ2 tests were used with the level of significance as P=0.05. The power of the statistical analysis was 80 percent. RESULTS: The prevalence of enuresis is 17.18% for boys and 11.82% for girls, and the overall prevalence is 14%.parental divorce, parental death, Physical punishment, living with step parents (parental divorce, computer game, watching horror movie and stress and domestic violence had significant correlation with enuresis. psychological factors such as death of a brother or sister, new baby, smoking in the family, drinking coffee and tea were factors that didn’t show any correlation with enuresis. CONCLUSION: The differences in the prevalence rate reported by various countries can be attributed to criteria and age ranges, definition of enuresis, genetic predisposition, and traditional with cultural background. Primary health caregivers must be educated according to their society’s condition to elicit a detailed history and explaining detrimental effects of enuresis and its association with body mass index to present true information about the medications and cares to the parents.
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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 | 0.001 |
| 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 | 0.000 | 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".