The Impact of Education and Residence on Current Use of Contraception in Ethiopian Women
Bibliographic record
Abstract
BACKGROUND: Maternal and infant mortality is high in Ethiopia. An underlying cause of maternal and infant mortality is unintended pregnancy, which is inversely associated with contraceptive use. Hence, the use of contraception can necessarily be increased to reduce the mortality rate of mother and infant. OBJECTIVES: This study aims to examine the effect of educational attainment and place of residence on contraceptive use among Ethiopian women of childbearing ages. METHODS: Data are obtained from the 2016 Ethiopian Demographic and Health Survey (EDHS) and total 10 223 women aged 15–49 years are analyzed in this study. Both unadjusted and adjusted multivariate logistic regression models are employed to examine the effect of exposure variables on contraceptive use. RESULTS: It can be seen that only around 36% of Ethiopian women used any of the contraceptive methods. In the case of contraceptive use, a significantly higher odds is observed for women with secondary or higher education with respect to those who were illiterate (e.g., OR=1.51, 95% CI: 1.19–1.93 after adjusting for total children ever born, number of living children, desire of more children, wealth index, respondent currently working, region and religion; and OR=2.53, 95% CI: 2.05–3.13 for unadjusted model). Urban women had also a significantly higher odds for contraceptive use than their rural counterparts (OR=1.89, 95% CI: 1.42–2.51 by adjusted model for the above confounders, and OR=2.22, 95% CI: 1.81–2.73 by unadjusted model). CONCLUSION: The findings reveal that both educational improvement and urbanization may increase the use of contraception among women in Ethiopia.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".