Understanding preconception care: a scoping review of knowledge, attitudes and practices among reproductive age individuals, healthcare workers and stakeholders in low- and middle-income countries
Bibliographic record
Abstract
OBJECTIVES: This scoping review aims to map existing evidence on knowledge, attitudes and practices (KAP) and barriers to preconception care in low- and middle-income countries. The primary objective is to identify key gaps and research priorities to guide future efforts to improve maternal and child health. DESIGN: This review followed Arksey and O'Malley's scoping review framework, with a comprehensive search across Medline, EMBASE, CINAHL and Scopus from inception to May 2025. Eligible studies included original research on preconception care (PCC), KAP in low- and middle-income countries (LMICs) without date restrictions. Two independent reviewers conducted screening in Covidence. Findings were presented in graphical, tabular and narrative formats, adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols Extension for Scoping Reviews (PRISMA-ScR) standard. SETTING: The review focused on PCC studies conducted in LMICs across various healthcare settings, emphasising primary and secondary levels of care. The geographical scope was global but limited to LMICs as defined by World Bank criteria. RESULT: A total of 62 studies were included in the review. Of these, 42 employed quantitative methods, 18 used qualitative approaches and 2 used a mixed-methods design. Regarding focus areas, 25 studies assessed knowledge, 14 assessed practices, 12 studies assessed KAP comprehensively and 10 assessed attitudes. Participants were mainly women of reproductive age (44 studies), with only five studies including men. Among healthcare providers, KAP varied, with midwives being the most frequently studied group. Stakeholders such as policymakers were notably under-represented. Identified barriers included limited training, cultural beliefs and inadequate policies. Facilitators highlighted were targeted education, spousal support and policy advocacy, emphasising the need for gender-sensitive and systemic interventions. CONCLUSION: LMICs face complex challenges in utilising PCC, influenced by socioeconomic, cultural, and healthcare system factors. To address these challenges, nuanced approaches incorporating intersectional perspectives and practical qualitative methodologies are essential for improving couples' and child health outcomes. TRIAL REGISTRATION NUMBER: The study protocol was registered in the Open Science Framework (OSF) on December 23, 2022, with DOI: 10.17605/OSF.IO/H3MK6.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".