E-learning in nursing education in Rwanda: A middle-range theory
Bibliographic record
Abstract
Background: The rapid development of technology has compelled tertiary institutions to devise innovative teaching strategies to meet the students’ needs and market’s demands. Recently, the Covid-19 pandemic is forcing educational instructions to shift from in-person to online learning. E-learning is one of the areas advancing rapidly and which provide promises in nursing education. The aim of this study was to develop a middle-range theory to guide the utilisation of an e-learning platform in nursing education in the context of Rwanda.Methods: A grounded theory approach, following Strauss and Corbin, was used. The study population included nurse educators, nursing students, Information and Communication Technologies (ICT) managers, and experts in e-learning and nursing education. The sample size consisted of 40 participants. Data were collected using in-depth interviews, focus group discussion and document analysis. Data analysis was guided by Strauss and Corbin’s grounded theory framework, which facilitated the middle-range theory development.Results: Implementation of e-learning in nursing education emerged as the central concept in this model. E-learning was viewed as a mechanism to advance the country’s political agenda to integrate technology in higher education, a tool to widen access to nursing education, a student-centred approach, and blended learning. The implementation of e-learning was facilitated by catalyst agents such as institutional support, e-readiness, partnerships and collaboration, policies and regulations, effective working learning management system, and bridging the digital divide. Integration of e-learning in nursing education was expected to improve nursing education quality and increase competent nurses and midwives graduates.Conclusions: This study highlights the importance of e-learning in nursing education. The adoption of the innovative, technology-enabled nursing education models would augment capacity to scale up nursing and midwifery education, enhance the quality and relevance of training, and adopt equity-focused policies. This model is a tool to facilitate the establishment of a supported network learning space in nursing education in a fluid and dynamically changing nursing practice context.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".