Access to continuous professional development for capacity building among nurses and midwives providing emergency obstetric and neonatal care in Rwanda
Bibliographic record
Abstract
BACKGROUND: Nurses and midwives are at the forefront of the provision of Emergency Obstetric and Neonatal Care (EmONC) and Continuous Professional Development (CPD) is crucial to provide them with competencies they need to provide quality services. This research aimed to assess uptake and accessibility of midwives and nurses to CPD and determine their knowledge and skills gaps in key competencies of EmONC to inform the CPD programming. METHODS: The study applied a quantitative, cross-sectional, and descriptive research methodology. Using a random selection, forty (40) health facilities (HFs) were selected out of 445 HFs that performed at least 20 deliveries per month from July 1st, 2020 to June 30th, 2021 in Rwanda. Questionnaires were used to collect data on updates of CPD, knowledge on EmONC and delivery methods to accessCPD. Data was analyzed using IBM SPSS statistics 27 software. RESULTS: Nurses and midwives are required by the Rwandan midwifery regulatory body to complete at least 60 CPD credits before license renewal. However, the study findings revealed that most health care providers (HCPs) have not been trained on EmONC after graduation from their formal education. Results indicated that HCPs who had acquired less than 60 CPD credits related to EmONC training were 79.9% overall, 56.3% in hospitals, 82.2% at health centres and 100% at the health post levels. This resulted in skills and knowledge gaps in management of Pre/Eclampsia, Postpartum Hemorrhage and essential newborn care. The most common method to access CPD credits included workshops (43.6%) and online training (34.5%). Majority of HCPs noted that it was difficult to achieve the required CPD credits (57.0%). CONCLUSION: The findings from this study revealed a low uptake of critical EmONC training by nurses and midwives in the form of CPD. The study suggests a need to integrate EmONC into the health workforce capacity building plan at all levels and to make such training systematic and available in multiple and easily accessible formats. IMPLICATION ON NURSING AND MIDWIFERY POLICY: Findings will inform the revision of policies and strategies to improve CPD towards accelerating capacity for the reduction of preventable maternal and perinatal deaths as well as reducing maternal disabilities in Rwanda.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 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".