Using students’ smartphones to learn a nursing skill: Students’ perspectives
Bibliographic record
Abstract
The increase in nursing students’ enrollment in post-secondary education, hospital restructuring and limited clinical placements have shifted nurses’ education to require more e-learning platforms. E-learning uses information and communication technologies to support interactions with content, learning activities and with others; and to facilitate self-reflection. Using smartphones’ video applications in a hybrid course can support learning. Most nursing students own smartphones and use them to create videos, however, their perspectives on using their smartphones to support learning a nursing skill is limited. This mixed method pilot study explored undergraduate nursing students’ perspectives on using their smartphones to record, and later receive feedback from their peers and faculty when learning a nursing skill. Twenty-six students completed questionnaires and seven students participated in a follow-up focus group. Two overarching themes emerged: (a) technical and (b) adaptive challenges. Students identified technical challenges in using their devices and how this influenced knowledge application. Others highlighted that the activity helped them to reflect and relate to self, others and their environments. The clinical, educational, ethical and research implications of this teaching-learning strategy will be discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".