Multi-stakeholder perspectives on access, availability and utilization of emergency obstetric care services in Lagos, Nigeria: A mixed-methods study
Bibliographic record
Abstract
Globally, Nigeria is the second most unsafe country to be pregnant, with Lagos, its economic nerve center having disproportionately higher maternal deaths than the national average. Emergency obstetric care (EmOC) is effective in reducing pregnancyrelated morbidities and mortalities. This mixed-methods study quantitatively assessed women's satisfaction with EmOC received and qualitatively engaged multiple key stakeholders to better understand issues around EmOC access, availability and utilization in Lagos. Qualitative interviews revealed that regarding access, while government opined that EmOC facilities have been strategically built across Lagos, women flagged issues with difficulty in access, compounded by perceived high EmOC cost. For availability, though health workers were judged competent, they appeared insufficient, overworked and felt poorly remunerated. Infrastructure was considered inadequate and paucity of blood and blood products remained commonplace. Although pregnant women positively rated the clinical aspects of care, as confirmed by the survey, satisfaction gaps remained in the areas of service delivery, care organization and responsiveness. These areas of discordance offer insight to opportunities for improvements, which would ensure that every woman can access and use quality EmOC that is sufficiently available.
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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".