Assessment of the Trends and Factors Associated With Unintended Pregnancy Among Women of Reproductive Age: An Analysis of the Nigerian Demographic Health Survey Between 2008 to 2018
Bibliographic record
Abstract
Background: Unintended pregnancy is a global public health issue with significant adverse effects which include health and economic consequences. Globally, there were 121 million unintended pregnancies annually between 2015 and 2019 among women of reproductive age between 15 and 49 mainly due to the non-uptake of modern contraceptives, harmful norms, stigma and lack of sexual and reproductive health care and information. Methods: We extracted information from the Nigeria Demographic Health Survey conducted in 2008, 2013, and 2018 to assess the trends and factors associated with unintended pregnancies among women of reproductive-aged 15-49. The descriptive summaries were presented using percentages and binomial logistic regressions for the inferential analysis. All analyses were computed using Stata 15.0 at a 5% level of significance and accounted for the complex survey nature as well as the population size. Results: The study included a total of 63,040 women of reproductive age. The prevalence of unintended pregnancy was highest among adolescents aged 15-19 years (15.1%, 95% CI: 13.9-16.5) and decreased with increasing age. The pooled adjusted model revealed that women had 11% lower odds of reporting unintended pregnancies in 2013 compared to 2008. Adolescent girls (aOR 2.48; 95%CI: 2.14-2.89) and young adults (aOR 1.86; 95%CI: 1.69-2.04) have higher odds of reporting unintended pregnancies compared to older women. Also, unmarried women had 9.8 times higher odds of reporting unintended pregnancies compared to ever-married women. Conclusions: The findings from this study highlight the need for further family planning educational programs and initiatives that support the uptake of effective contraceptive methods to reduce the likelihood of unintended pregnancy and improve women's sexual and reproductive health while considering regional variations within the country to ensure tailored interventions that address specific needs within each region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".