Utilization of technology to provide on-the-job trainings on Emergency Obstetric and Neonatal Care: Perspectives of nurses and midwives working in Rwanda’s remote health facilities
Bibliographic record
Abstract
INTRODUCTION: One of the targets for the third sustainable development goals is to reduce worldwide maternal mortality ratio (MMR) to less than 70 deaths per 100,000 live births by 2030. To address issues affecting women and the newborns during childbirth and postnatal period, concerted efforts from governments and their stakeholders are crucial to maximize the use of technology to enhance frontline health professionals' skills to provide the emergency obstetric and newborn care (EmONC). However, no study has garnered nurses' and midwives' perspectives regarding the application of technology-enhanced learning approach to provide on-the-job Continuous Professional Development (CPD) and factors that may influence the application of this training approach in the Rwandan context. METHODS: The study collected data from nurses and midwives from forty (40) public health facilities in remote areas nationwide. The study applied a qualitative descriptive design to explore and describe nurses' and midwives' perspectives on the feasibility and acceptability of technology enhanced learning approaches such as e-learning, phone-based remote training, and other online methods to provide trainings in EmONC. Two focus group discussions with EmONC mentors, two with nurses and midwives were conducted. Twelve key informant interviews were conducted. Participants were selected purposively. In total, 54 individuals were included in this study. A thematic approach was used to analyse data. RESULTS: Nurses and midwives highlighted the need to provide refresher trainings about the management of pre-eclampsia. Most of the EmONC trainings are still provided face-to-face and the use of technology enhanced learning approaches have not yet been embraced in delivering EmONC CPDs for nurses and midwives in remote areas. Nurses and midwives found the first developed prototype of smartphone app training of the EmONC acceptable as it met the midwives' expectations in terms of the knowledge and skills' gap in EmONC. CONCLUSION: Although the newly developed application was found acceptable, further research involving practical sessions by nurses and midwives using the developed application is needed to garner views about the ease of use of the application, relevance of the EmONC uploaded content on the app, and needed improvements on the app to address their needs in EmONC.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".