Correlation between Social Determinants of Health and Women’s Empowerment in Reproductive Decision-Making among Iranian Women
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Women empowerment is one of millennium development goals which is effective on fertility, population's stability and wellbeing. The influence of social determinants of health (SDH) on women empowerment is documented, however the correlation between SDH and women's empowerment in fertility has not been figured out yet. This study was conducted to assess correlation between social determinants of health and women's empowerment in reproductive decisions. MATERIAL & METHODS: This was a descriptive-correlation study on 400 women who attended health centers affiliated to Shahid Beheshti University of Medical Sciences Tehran-Iran. Four hundred women were recruited using multistage cluster sampling method. The tools for data collection were 6 questionnaires including; 1) socio-demographic characteristics 2) women's empowerment in reproductive decision-making, 3) perceived social support, 4) self-esteem, 5) marital satisfaction, 6) access to health services. Data were analyzed by SPSS-17 and using Pearson and Spearman correlation tests. RESULTS: Results showed 82.54 ± 14.00 (Mean±SD) of total score 152 of women's empowerment in reproductive decision making. All structural and intermediate variables were correlated with women's empowerment in reproductive decisions. The highest correlations were demonstrated between education (among structural determinants; r= 0.44, P< 0.001), and Self-esteem (among intermediate determinants; r= 0.34, P< 0.001) with women's empowerment in fertility decision making. CONCLUSION: Social determinants of health have a significant correlation with women's empowerment in reproductive decision-making.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".