Preconception care practice among pregnant women attending antenatal care at Wachemo University, Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital, southern Ethiopia, 2022: a mixed-methods study
Bibliographic record
Abstract
Background Preconception care includes biomedical, behavioral, and social health interventions for women and couples before conception, aiming to improve their overall health status. Despite its importance, studies conducted in Ethiopia reveal that the practice remains unacceptably low; this emphasizes the need for further investigation, particularly through mixed-methods studies incorporating women's perspectives. Methods An institution-based, cross-sectional study with concurrent triangulation was conducted at Wachemo University, Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital between 1 April and 30 June 2022. Quantitative data were collected using a systematic random sampling method, while qualitative data were obtained through purposive sampling. A structured, interviewer-administered questionnaire was used to collect data from 332 eligible antenatal care clients. The data were entered into EpiData and analyzed using SPSS. Bivariate and multivariate analyses were performed to identify factors associated with the practice of preconception care. A 95% confidence interval (CI) and p-values <0.05 were considered statistically significant. Thematic analysis of qualitative data was performed using ATLAS.ti version 7. Results This study showed that 104 women (31.3%) (95% CI: 26.5–36.5) engaged in good preconception care practices. Factors significantly associated with good practices included attending college or university [adjusted odds ratio (AOR) = 3.52, 95% CI: 1.14–10.87], having a history of adverse pregnancy outcomes (AOR = 4.82, 95% CI: 2.20–10.58), receiving support from one’s husband (AOR = 2.45, 95% CI: 1.05–5.73), and having good knowledge of preconception care (AOR = 4.52, 95% CI: 2.11–9.68). The qualitative analysis revealed that client-related and health facility-related factors influenced the practice of preconception care. Conclusion Nearly 7 out of 10 women in this study became pregnant without the utilization of any component of preconception care. To improve the practice of preconception care, it is essential to raise awareness about its benefits among all women of reproductive age. Future efforts focusing on knowledge dissemination and awareness creation to improve partner support are crucial to enhancing preconception care practices.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 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".