The structural factors of social determinants of health on Iranian women's childbearing: a systematic review study
Bibliographic record
Abstract
Introduction: Childbearing is an important phenomenon in demographic movements and the basis of sustainable development in countries with low replacement fertility rates. The present study was performed with aim to summarize the existing knowledge about the effectiveness of structural factors of social determinants of health on childbearing of Iranian women. Methods: In this systematic review, observational Persian and English studies published from 1/1/2010 to 23/1/2022 were included in the study. Studies were searched in Magiran, SID, Embase, google scholar, Scopus, web of science, and PubMed databases using related keywords including: Reproductive Behavior, Childbearing, Socioeconomic Factors, Ethnic Groups, Culture, Structural Determinants of Health and Iran. The Newcastle-Ottawa tool was used to evaluate the quality of the articles. Results: In this review study, 36 studies which met the inclusion criteria were included in the study, and health structural factors (education, income, occupation, ethnicity, and culture) on women's childbearing were examined. Women's childbearing is related to these structural factors of health. In this review study, the highest frequency was related to women's education; so that 18 articles showed the negative effect of education on childbearing. Conclusion: The results of the present systematic review showed the effect of structural social determinants of health (education, income, occupation, ethnicity, and culture) including women's education on childbearing. This confirms that if population policies can implement programs that make the mother's role compatible with the continuation of women's education after marriage, they can be much more effective.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".