Knowledge of healthcare providers on preconception care in east Africa: Systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Preconception care involves measures to enhance a woman's physical, psychological, and nutritional health before pregnancy. Despite various observational studies assessing healthcare practitioners' knowledge of preconception care in East Africa, the overall pooled knowledge level remains unclear, and the studies often report inconsistent associated factors. This systematic review and meta-analysis aimed to determine the aggregated knowledge of preconception care among healthcare providers in East Africa and identify influencing factors. Method: We searched studies using PubMed, Scopus, Embase, and Google Scholar that were published between January 01, 2018 and November 30, 2024. This study used the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. The quality of studies was evaluated using the modified Newcastle-Ottawa quality assessment tool. The data were extracted by two authors independently using Microsoft Excel and analyzed by Stata version 17. A random effects model was applied to calculate the pooled level of knowledge and its associated factors. The International Prospective Register of Systematic Review registration number for the review was CRD42024608878. Results: A total of 12 studies comprising 4892 participants were involved in this meta-analysis. The pooled knowledge of preconception care among healthcare providers was 56% (95% CI: 45%-66%). This study showed that gender (odds ratio (OR) = 1.35), educational level (OR = 3.52), monthly salary (OR), work experience (OR = 1.77), Internet access (OR = 3.41), ever read the preconception care guideline (OR = 2.77), having Smartphone (OR = 1.70), working institution (OR = 2.05), Training on HIV testing and management (OR = 4.28), training on providing alcohol or tobacco cessation service (OR = 1.14), the presence of a library in a working health facility (OR = 1.98), taking training on preconception care education and counseling (OR = 3.44) were significant factors associated with knowledge of preconception care. Conclusion: The findings indicate that healthcare providers in East Africa have limited knowledge of preconception care. Gender, educational level, monthly salary, previous work experience, internet connection, awareness of preconception care policy, smartphone possession, type of work schedule, prior HIV testing, and management training, library access in healthcare facilities, and involvement in preconception care training meetings and counseling sessions are significant factors of the knowledge of preconception care among healthcare providers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 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 | 0.000 |
| 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".