Using students’ smartphones to learn a nursing skill: Students’ perspectives
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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