The feasibility of using mobile devices in nursing practice education
Bibliographic record
Abstract
This paper focuses on an exploratory evaluation of the use of m-learning in nursing education. We report on Stage 2 of the formative evaluation of a project to integrate mobile learning into the Bachelor of Science Nursing curriculum in a Western Canadian college program. Third year nursing students and instructors used Hewlett Packard iPAQs for five weeks in a practice education course in April - May, 2007. The iPAQs provided WiFi and GPRS wireless capability and were loaded with programs such as Microsoft Office Mobile 6.0 and the 2007 Lippincott Nursing Drug Guide. Our participants found the mobile devices supplied to be easy to learn and comfortable to use. They felt that the devices were readily portable and the screen size sufficient for programs designed for this medium. However, they nonetheless had difficulty using the wireless connectivity afforded by the devices and found that, despite an initial orientation, they did not have time to fully learn the devices in the context of a busy course. We concluded that it was feasible to implement mobile devices in nursing practice education, but that further investigation is needed on the use of m-learning for communication and interactive purposes.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".