Prevalence and predictors of unintended pregnancy among antenatal women in Sierra Leone: A cross-sectional study
Bibliographic record
Abstract
Unintended pregnancy remains a major contributor to adverse maternal and child health outcomes in sub-Saharan Africa, yet recent facility-based data from Sierra Leone are scarce. We aimed to determine the prevalence and identify factors associated with unintended pregnancies among antenatal clinic attendees at a major tertiary maternity hospital in Sierra Leone. We surveyed 1005 first-visit antenatal attendees at Princess Christian Maternity Hospital in Freetown between 19 March and 30 June 2024, using systematic sampling and multivariable logistic regression to identify independent predictors. Overall, 31.8% of women (95% CI 29.0 - 34.7) reported the current pregnancy as unintended; most were mistimed (30.0%) and the remainder unwanted (1.8%). Higher odds of unintended pregnancy were observed among women younger than 20 years (aOR 3.57, 95% CI 2.30 - 5.55), those who were unmarried (aOR 3.73, 95% CI 2.60 - 5.36), and those who were unemployed or students (aOR 1.74, 95% CI 1.25 - 2.42). Open partner communication about pregnancy (aOR 0.10, 95% CI 0.07 - 0.16) and partner desire for the pregnancy (aOR 0.05, 95% CI 0.03 - 0.09) were strongly protective. Nearly one in three pregnancies at Sierra Leone's principal referral maternity hospital is therefore unintended, with the burden falling on adolescents, unmarried women, and those with limited economic means. Interventions that integrate youth-friendly contraception services, partner-centred counselling, and broader female economic empowerment should be prioritised to reduce unintended pregnancies and improve maternal and child health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".