Mapping Evidence on the Determinants of Postnatal Care Knowledge among Postpartum Women in sub-Saharan Africa: A Literature Review
Bibliographic record
Abstract
Maternal and neonatal deaths continue to pose significant public health challenges globally. In 2020, low-to-middle-income countries accounted for over 95% of all maternal deaths. Sub-Saharan Africa (SSA) is the region most severely impacted, accounting for 70% of global maternal deaths in 2020. Most of the maternal deaths and about a third of child deaths occur in the postnatal period. These unnecessary deaths can be avoided if postpartum women have adequate knowledge about postnatal care (PNC). This literature review’s aim was to determine the factors that influence PNC knowledge among postpartum women in SSA. The methodology of this literature review was loosely guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) statement. Peer-reviewed articles describing determinants of PNC knowledge among postpartum women in SSA published in English between 2013 and 2023 were searched using several search engines. All the twenty-five articles used in this literature review reported on quantitative cross-sectional studies. Some of the individual-level determinants of PNC knowledge identified in this review include place of residence, age of the mother, marital status, educational status of the mother, and the socio-economic status of the woman while the health system-level determinants include distance to a healthcare facility, source of PNC information, place of delivery, and previous maternal healthcare service experience. To improve PNC knowledge of postpartum women, we recommend developing rural areas through improving transport networks, improving the socio-economic status of women, and devising strategies to increase maternal and child health services utilization.
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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".