Reducing maternal and newborn mortality in Nigeria—a qualitative study of stakeholders’ perceptions about the performance of community health workers and the introduction of community midwifery at primary healthcare level
Bibliographic record
Abstract
BACKGROUND: Rural communities in Nigeria account for high maternal and newborn mortality rates in the country. Thus, there is a need for innovative models of service delivery, possibly with greater community engagement. Introducing and strengthening community midwifery practice within the Nigerian primary healthcare system is a clear policy option. The potential of community midwifery to increase the availability of skilled care during pregnancy, at birth and within postpartum periods in the health systems of developing countries has not been fully explored. This study was designed to assess stakeholders' perceptions about the performance of community health workers and the feasibility of introducing and using community midwifery to address the high maternal and newborn mortality within the Nigerian healthcare system. METHODS: This study was undertaken in two human resources for health (HRH) project focal states (Bauchi and Cross River States) in Nigeria, utilizing a qualitative research design. Interviews were conducted with 44 purposively selected key informants. Key informants were selected based on their knowledge and experience working with different cadres of frontline health workers at primary healthcare level. The qualitative data were audio-recorded, transcribed and then thematically analysed. RESULTS: Some study participants felt that introducing community midwifery will increase access to maternal and newborn healthcare services, especially in rural communities. Others felt that applying community midwifery at the primary healthcare level may lead to duplication of duties among the health worker cadres, possibly creating disharmony. Some key informants suggested that there should be concerted efforts to train and retrain the existing cadres of community health workers via the effective implementation of the task shifting policy in Nigeria, in addition to possibly revising the existing training curricula, instead of introducing community midwifery. CONCLUSION: Applying community midwifery within the Nigerian healthcare system has the potential to increase the availability of skilled care during pregnancy, at birth and within postpartum periods, especially in rural communities. However, there needs to be broader stakeholder engagement, more awareness creation and the careful consideration of modalities for introducing and strengthening community midwifery training and practice within the Nigerian health system as well as within the health systems of other developing countries.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".