Determinants of Unintended Pregnancy and Unmet Need for Contraception among Reproductive-Age Women in Nigeria
Bibliographic record
Abstract
Background: Globally, a quarter of unintended pregnancies end in unsafe abortions which is one of the leading causes of maternal mortality. This study aimed to investigate the determinants of unintended pregnancy and the unmet need for contraception among reproductive-age women in Nigeria.Methods: Data were obtained from the 2018 Nigerian Demographic and Health Survey. A total of 20,655 women of reproductive age were included. Unmet need for contraception was the main outcome variable. Bivariate and multiple logistic regressions were carried out to predict the determinants. SPSS version 27.0 was used for data analysis. Statistical significance was set at p < 0.05.Results: The respondents’ mean age was 29.7±7.1years, 36.7% had their first child before 18years, and 93.9% were married. 18.5% desired no more children and only 17.5% were currently using contraceptives. 11.8% admitted to having unintended pregnancy and 74.2% reported no unmet need for contraception. Respondents who were 18years and above at first birth were 14.7% less likely to have unintended pregnancy compared with those who were below 18years at their first birth (OR=0.853, CI=0.764–0.952). Respondents who had ever terminated a pregnancy were 1.3 times more likely to have an unintended pregnancy than those who had never terminated a pregnancy (OR=1.251, CI=1.101–1.422). Conclusion: Many women reported unmet contraceptive needs, with some admitting to having unintended pregnancies. Addressing this is vital for reducing unintended pregnancies, which will contribute to improved health status for mothers and their babies and facilitate the attainment of maternal and child health-related sustainable development goals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".