A Study of the Impact of an Educational Intervention on Nurse Attitudes and Behaviours toward Mobile Device Use in Hospital Settings
Bibliographic record
Abstract
Introduction: Mobile applications (apps) provide nurses with evidence-based information at the bedside. Librarians encourage app use by purchasing licenses and promoting their features. While many high-quality nursing apps exist, there is inconsistency in published reports on whether nurses use them in patient care. The aim of this research is to describe the use of mobile apps by nurses at two urban hospitals and to examine the impact of educational sessions led by hospital librarians and educators on nurse usage, attitudes and behaviour as they relate to mobile apps.Methods: Phase I consisted of a descriptive, cross-sectional survey of in-patient nurses to determine mobile app use and attitudes. Phase II involved a one-group pre/post-test design to examine the impact of education sessions led by librarians and hospital educators on nurse attitudes, usage and behaviours. A post-intervention focus group captured thoughts on using mobile apps at the bedside.Results: Results indicate that most nurses who have a personal mobile device are interested in using them at the bedside though few are currently doing so. While nurses cite many conveniences and uses, they also highlight a number of barriers associated with using mobile devices that must be addressed in order to realize the benefits in patient-centred care.Discussion: Hospital librarians and educators should work together to provide the education and support nurses require to realize the benefits of using apps at the bedside. Larger studies are needed to determine the impact of educational sessions on patient and health provider satisfaction with mobile device use.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".