Use of iPhones by Nurses in an Acute Care Setting to Improve Communication and Decision-Making Processes: Qualitative Analysis of Nurses’ Perspectives on iPhone Use
Bibliographic record
Abstract
BACKGROUND: Smartphones and other mobile devices are having and will continue to have an impact on health care delivery in acute settings in Australia and overseas. Nurses, unlike physicians, have been slow to adopt these technologies and the reasons for this may relate to the status of both these professions within the hospital setting. OBJECTIVE: To explore nurses' perspectives on iPhone use within an acute care unit. We examined their experiences and views on how this device may improve communication and decision-making processes at the point of care. METHODS: Two focus group discussions, using a semistructured interview, were conducted over the trial period. The discussions focused on the nurses' experiences regarding ease of use, features, and capabilities of the device. The focus groups were recorded, transcribed, and analyzed using semistructured interview questions as a guide. RESULTS: The positive findings indicated that the iPhones were accessible and portable at point of care with patients, enhanced communication in the workplace, particularly among the nurses, and that this technology would evolve and be embraced by all nurses in the future. The negatives were the small screen size when undertaking bedside education for the patient and the invasive nature of the device. Another issue was the perception of being viewed as unprofessional when using the device in real time with the patients and their family. CONCLUSIONS: The use of iPhones by nurses in acute care settings has the potential to enhance patient care, especially through more effective communication among nurses, and other health care professionals. To ensure that the benefits of this technology is woven into the everyday practice of the nurse, it is important that leaders in these organizations develop the agenda or policy to ensure that this occurs.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".