Smartphones in Clinical Nursing Practice: A Multiphased Approach to Implementation and Deployment.
Bibliographic record
Abstract
Students in the undergraduate nursing program at the University of Calgary - Qatar are required to work with patients in clinical settings under faculty supervision.. One of the main goals of clinical courses is to provide students with the opportunity to learn in context and ‘just-in-time’, a much more realistic and memorable learning experience. During clinical placements, students need to acquire additional information about illnesses, medication and patient care on site. The current research was conducted to determine if properly selected smartphone technology and accompanying software would help provide students with information they needed in a just-in-time fashion and if this would have a positive impact on their learning. A multi-phased study was developed to (1) determine the impact of smartphone and software deployment in clinical courses on student learning and to determine barriers and issues that may inhibit success [Phase 1] and (2) to use the knowledge gained in phase 1 to address these issues and barriers by optimizing e.g., deployment strategies [Phase 2]. Findings from phase 1 indicate success in terms of learning outcomes while also showing that students would prefer to use their own smartphones. Phase 2 is currently underway and will result in the development of implementation strategies based on evidence gained from phase 1 and mobile technology usage pattern survey (ECAR).
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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 | 0.072 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".