Development of Children in Iran: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: In order to gain a better perspective of the developmental status of children in different regions of Iran, this study was carried out to determine the prevalence and the factors impacting child development in Iranian studies. MATERIALS & METHODS: Articles published in Iranian and international journals indexed in the SID, PubMed, Scopus and Magiran databases from 2001-2015 were systematically reviewed using standard and sensitive keywords. After evaluating the quality of 155 articles in the initial search, 26 articles were analyzed according to the inclusion criteria. After investigations, meta-analysis was done for six studies and the results were combined using Random Effects model, and the heterogeneity of studies was evaluated using the I2 index. Data analysis was performed using STATA version 11.2. RESULTS: Eagger & Beggs tests, respectively with 0/273 & 0/260 did not confirm the probability of publication bias in the data, but heterogeneity in studies was confirmed (p˂0/001). On such basis, the pooled prevalence of developmental disorder based on Random Effect model was calculated to be 0.146, CI (0/107-0/184). The prevalence of developmental disorders in children in the studies reviewed was reported between 7 to 22.4%. The most important risk factors were in SES )Socio Economic Status) and Prenatal, Perinatal, Neonatal &Child groups. CONCLUSION: More extensive studies and early intervention with respect to causes of developmental delay in children seems necessary.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".