Designing and delivering digital learning (e-Learning) interventions in nursing and midwifery education: A systematic review of theories
Bibliographic record
Abstract
AIMS /OBJECTIVES: To identify and synthesise theories that support the design and delivery of digital learning interventions in nursing and midwifery education. BACKGROUND: A range of educational and other theories are used to support nursing and midwifery education, including when e-learning interventions are being designed and delivered. However, there is a limited understanding of how theory is applied across the wide range of digital learning interventions to inform pedagogical research and practice. DESIGN: A systematic review. METHODS: CINAHL, ERIC, MEDLINE and PubMed were searched using key terms. Studies were screened by independent reviewers checking the title, abstract and full text against eligibility criteria. Due to the theoretical focus of the review, critical appraisal was not undertaken. Data were extracted and synthesised using a descriptive approach. RESULTS: Thirty-four studies were included. Twenty theories were identified from a range of scientific disciplines, with the Technology Acceptance Model and Theory of Self-Efficacy employed most often. Theoretical frameworks were used to inform and explain how the digital learning interventions were designed or implemented in nursing and midwifery education. The sample were mainly undergraduate nursing students and the digital learning interventions encompassed animation, blended approaches, general technologies, mobile, online, virtual simulation and virtual reality applications which were used mainly in university settings. CONCLUSIONS: This systematic review found a range of theories that support the design and delivery on digital learning interventions in nursing and midwifery education. While a single theory, the Technology Acceptance Model, tended to dominate the literature, the evidence base is peppered with numerous theoretical models that need to be examined more rigorously to ascertain their utility in improving the design or implementation of digital forms of learning to improve pedagogical research and practice in nursing and midwifery.
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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 | 0.047 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".