Technology usage for teaching and learning in nursing education: An integrative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The increasing availability of technology devices or portable digital assistant devices continues to change the teaching-learning landscape, including technology-supported learning. Portable digital assistants and technology usage have become an integral part of teaching and learning nowadays. Cloud computing, which includes YouTube, Google Apps, Dropbox and Twitter, has become the reality of today's teaching and learning and has noticeably improved higher education, including nursing education. OBJECTIVES: The aim of this integrative literature review was to explore and describe technology usage for teaching and learning in nursing education. METHOD: A five-step integrative review framework by Whittemore and Knafl was used to attain the objective of this study. The authors searched for both empirical and non-empirical articles from EBSCOhost (health information source and health science), ScienceDirect and African Journals Online Library databases to establish what is already known about the keywords. Key terms included in literature search were coronavirus disease 2019 (COVID-19), digital learning, online learning, nursing, teaching and learning, and technology use. RESULTS: Nineteen articles were selected for analysis. The themes that emerged from this review were (1) technology use in nursing education, (2) the manner in which technology is used in nursing education, (3) antecedents for technology use in nursing education, (4) advantages of technology use in nursing education, (5) disadvantages of technology use in nursing education and (6) technology use in nursing education amidst COVID-19. CONCLUSION: Technology in nursing education is used in both clinical and classroom teaching to complement learning. However, there is still a gap in its acceptance despite its upward trend.Contribution: The findings of this study contribute to the body of knowledge on the phenomenon of technology use for teaching and learning in nursing education.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it