A Path Analysis of Factors Influencing the First Childbearing Decision-Making in Women in Shahrood in 2014
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Normal fertility follows a set of biological, social and cultural rules and regulations; controlled fertility, however, follows the rules and regulations of the family. The present study was therefore conducted to identify the factors influencing the first childbearing decision-making in women living in Shahroud, Iran, in 2014. MATERIALS & METHODS: The present descriptive cross-sectional study was conducted on 300 randomly-selected pregnant women admitted to health centers in Shahroud. The utilized data collection tools included a demographic and obstetrics questionnaire, a quality of life questionnaire, the ENRICH Marital Satisfaction Scale, the Snyder Hope Scale and the Multidimensional Scale of Perceived Social Support. Data were analyzed in SPSS-17 and the direct or inverse effects of the factors influencing the first childbearing decision-making were examined in AMOS-20. RESULTS: The results obtained revealed marital age to have the highest degree of correlation with the first childbearing decision-making in women (r=0.90 and P<0.001).Once the statistically insignificant paths were eliminated, marital age was found to have the highest direct effect (β=0.63) on the first childbearing decision-making, followed by other factors including economic status (β=0.07), hopefulness (β=-0.07) and quality of life (β=-0.05). The inverse effects of marital age (β=0.01), social support (β= -0.01) and quality of life (β=-0.01) on the first childbearing decision-making were found to be significant in women (P<0.001). CONCLUSION: Many factors are involved in the process of childbearing decision-making, including individual factors (marital age, hopefulness and quality of life), familial factors (marital satisfaction) and social factors (social support). Healthcare institutions and policymakers should adopt strategies that can help couples bear their desired number of children within an appropriate time frame through ameliorating their social, economic and familial conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".