Perception of registered nurses and midwives on maternal health education in Nigeria
Bibliographic record
Abstract
Objective: To assess the views of Nigerian Nurses and Midwives on Maternal Health Education (MHE) and the barriers to its implementation.Methods: A total of 238 qualified nurses and midwives who participated in Mandatory Continuing Professional Development Programme (MCPDP) in South-eastern state of Nigeria voluntarily completed the self-administered research questionnaire. To avoid receiving duplicate copies of the questionnaires, all were serially numbered and all personal identifiers were removed. Of 348 participants that completed the questionnaires, only 238 met the inclusion criteria which included experience in antenatal clinics and qualifications in midwifery.Results: The majority of the study participants (86%) had both nursing and midwifery qualifications and the majority (98%) believed that MHE is beneficial to pregnant mothers particularly in reducing maternal morbidity and mortality (95.3%). A high percentage of the respondents (92%) agreed that MHE should be intensified for pregnant mothers in their work places. The identified major barriers to MHE include attitude of some health professionals (79%), some cultural practices (77%), inadequate economic resources (75%) and insufficient health personnel (71%). 18% of the respondents agreed that the hospital policy of their work places does not promote MHE.Conclusions: This study has demonstrated that nurses and midwives are aware of the importance of MHE in reducing maternal mortality and morbidity. There are still negative perceptions on the preparedness of the healthcare institutions towards MHE coupled with economic and cultural barriers. We recommend integrated MHE in the antenatal care plans of the pregnant woman.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".