Socioeconomic Status and Stress Rate during Pregnancy in Iran
Bibliographic record
Abstract
BACKGROUND: Stress during pregnancy can have serious adverse outcomes on the mother, the fetus, newborn, children and even adolescents. Socioeconomic status has been recognized as a predictor of stress amongst pregnant women. OBJECTIVES: The first aim of this study was to investigate the role of socioeconomic status in pregnancy stress rates. The second aim was to examine the most important items of socioeconomic status including monthly family income, husband occupational status as well as mother's educational level and their influence on the rate of maternal stress. METHODS: This study was cross-sectional research and was conducted on 210 pregnant women in three trimesters of pregnancy who attended Shahryar hospital for prenatal care between August-October 2012. They completed two questionnaires of Socioeconomic Status and Specific Pregnancy Stress. Collected data were analyzed by SPSS version 19 including T-test, one-way ANOVA and Spearman correlation. RESULTS: In this study, we considered family income, education and husbands' occupations as the most important variables which may influence perceived stress during pregnancy. The mean age of women was 27±4.8 years. The final result showed that there is no significant relationship between SES and pregnancy stress level (P > 0.05), while we found a significant relationship, as well as indirect correlation between husbands' occupational status and pregnancy stress (P < 0.05, r= -0.364). CONCLUSION: Further investigations may be considered for extending the results to all pregnant women. Thus, health officials and universities should finance other studies to investigate this fact and whether other dimensions of SES influence pregnancy stress levels or not.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".