Preterm Delivery and Psycho–Social Determinants of Health Based on World Health Organization Model in Iran: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Preterm delivery is still the primary cause of mortality and morbidity in infants, which shows a problematic condition in the care of pregnant women all over the world. This review study describes prevalence and psycho - socio-demographic as well as obstetrical risk factors related to live preterm delivery (PTD) in the recent decade in Iran. METHODS: A narrative review was performed in Persian and international databases including PubMed, SID, Google Scholar, Iran Medex, Magiran and Irandoc from 2001 to 2010 with following keywords: preterm delivery and pregnancy outcomes with (prevalence, socioeconomic condition, structural determinant, Intermediary determinants, Psychosocial factor, Behavioral factor and Maternal circumstance, Health system). All of article was reviewed then categorized based on WHO model. RESULTS: Totally 52 article were reviewed and 35 articles were selected, of which 26 were cross-sectional or longitudinal, 9 were analytical (cohort or case-control). The prevalence rates of preterm delivery in different cities of Iran were reported between 5.6% in Quom to 39.4% in Kerman. The most common social factors in structural determinant were educational level of mother, and in intermediary determinants were Psychosocial factor (maternal anxiety and stress during pregnancy), Behavioral factor and Maternal circumstance (violation and trauma) and in Health system, lack of prenatal care. CONCLUSION: The prevalence rate of preterm delivery is a matter of concern. Since many psycho-social factors may affect on the condition and its high rate in poor communities might reveals a causal relationship among biological and psychosocial factors, performing etiological investigations is recommended.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".