Knowledge and Practice of Existing Guidelines for the Management of Obstetric Complications among Healthcare Providers in Selected Health Facilities, Kwara State, Nigeria
Bibliographic record
Abstract
This study assessed the level of knowledge and practice of existing guidelines for the management of obstetric complications among healthcare providers in selected primary and secondary healthcare facilities in Kwara State, Nigeria, and examined the relationship between socio-demographic characteristics and guideline utilization. A descriptive survey design was employed among 150 nurses and midwives selected through a multistage sampling technique. Data were collected using a validated and reliable structured questionnaire and analyzed with SPSS version 25 using descriptive and inferential statistics. Results showed that healthcare providers demonstrated an overall high level of knowledge and practice of obstetric management guidelines, particularly in the management of postpartum haemorrhage, obstructed labour, uterine rupture, and cord prolapse. However, notable gaps were identified in knowledge and practice related to pre-eclampsia and eclampsia, including diagnostic criteria and pharmacological management. Inferential analysis revealed statistically significant relationships between healthcare providers’ age, professional qualifications, and years of experience, and both their knowledge and practice of existing guidelines (p < 0.05). The study concludes that although guideline awareness and application are generally strong among healthcare providers in Kwara State, targeted capacity-building interventions are required to address identified deficiencies. Strengthening continuous professional education, mentorship, and access to updated guidelines is essential to ensure consistent, evidence-based obstetric care and to improve maternal health outcomes. Keywords: Obstetric complications, Clinical guidelines, Healthcare providers, Knowledge, Practice, Maternal health,
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".