Open and Distance Learning Programs for Nursing and Midwifery Education in East Africa: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: In the face of growing modernity and the coronavirus disease 2019 (COVID-19) pandemic, open and distance learning (ODL) is considered to play an important role in increasing access to education worldwide. There is a robust evidence base demonstrating its cost effectiveness in comparison with conventional class-based teaching; however, the transition to this new paradigm of learning for nursing and midwifery courses has been difficult in low-income countries. While there are notable efforts to increase internet and education access to health care professionals, not much is known about ODL for nurses and midwives in East African countries. OBJECTIVE: The objective of this scoping review is to understand whether ODL programs for nursing and midwifery education exist, the drivers of their adoption, their implementation, the topics/courses covered, their acceptability, and their impacts in East African countries. METHODS: The scoping review methodology employs the framework developed by Arksey and O'Malley. Using an exploratory approach, a two-stage screening process consisting of a title and abstract scan and a full-text review will be used to determine the eligibility of articles. To be included, articles must report on an existing ODL initiative for nurses and midwives in Uganda, Tanzania, and Kenya. All articles will be independently assessed for eligibility by pairs of reviewers, and all eligible articles will be abstracted and charted in duplicate using a standardized form. RESULTS: Details of ODL for nursing and midwifery education initiatives and study outcomes will be summarized in a table. The extracted data will undergo exploratory descriptive analysis, and the results will be classified into learner and clinical outcomes. CONCLUSIONS: Evidence on ODL for nursing and midwifery education will inform the ongoing development and restructuring of health care professional education in East Africa amidst the COVID-19 pandemic. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/17765.
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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 | 0.093 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.068 | 0.015 |
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".