Effectiveness of m-Learning in Enhancing Knowledge Retention for Nurses’ Lifelong Learning: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: The current Information and Communication Technologies, digital literacy, and ease of access to communication and information devices by nurses provide them with new ways and intention to access information for technical-scientific updating, ensuring the quality and safety of health care. M-learning offers a flexible and accessible alternative for continuing professional education, overcoming barriers such as time constraints and financial burden. OBJECTIVE: To evaluate the effectiveness of m-learning in nurses' knowledge retention of Chronic Obstructive Pulmonary Disease self-management, using a Massive Open Online Course with integrated virtual clinical simulation. METHODS: A quasi-experimental pre- and post-test study was conducted, with no control group, with 168 nurses from a Portuguese hospital. The intervention included an asynchronous online course with 13 modules. Knowledge retention was assessed by comparing the mean scores before and after the course. RESULTS: The results indicated a significant increase in knowledge retention. The participants' average score increased from 59.97% in the initial assessment to 84.05% in the final assessment (p<.001). Nurses with a master's degree exhibited a higher level of basic knowledge than those with a bachelor's degree. The course completion rate was 93.45%, reflecting significant engagement attributed to gamification and clinically relevant content. CONCLUSIONS: M-learning is useful in nurses' lifelong learning, offering flexibility and more effective support for clinical practice. Integrating virtual simulation and gamification boosted motivation and reduced drop-out rates, highlighting the potential of m-learning in lifelong learning in healthcare. This study confirms the effectiveness of m-learning in improving knowledge retention in nursing. This strategy is a valuable approach to lifelong learning, promoting quality and safety in delivering healthcare.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".