A Review of the Effect of Nurses’ Use of Smartphone to Improve Patient Care
Bibliographic record
Abstract
Introduction: Nurses in the acute-care setting use touchscreen smartphones (eg. iPhones) to facilitate patient care. However, on duty nurses also use smartphones to access social media, text, and shop online. The overall benefit of nurses’ use of smartphones to patient care is unclear. We conducted a systematic review to examine the use of smartphones by acute-care nurses and how that influences patient care. Methods: We searched Embase, MEDLINE, PsycINFO, CINAHL, and PubMed databases using the key words “smartphone,” “nurse,” “patient care” and “quality of care” to identify articles focusing on smartphone use by nurses in acute care setting. Only 274 articles were initially identified. Fourteen articles remained after applying inclusion criteria such as nurses in acute care setting, written in English, and excluding those addressing the use of smartphones by non-nurses. Results: We identified six themes encompassing advantages and disadvantages of smartphone use by nurses in the acute care setting. Theme 1: enhanced interprofessional communication. Theme 2: easy and quick access to clinical information (eg. medications). Theme 3: improved time-management. Theme 4: reduction of work stress. Disadvantages were: Theme 5: distraction from work, and Theme 6: the appearance of unprofessionalism. Conclusions: Smartphone use by nurses in the acute care setting impacts how they provide daily care to their patients. Benefits of smartphone use include: improved patient safety, more effective communication between healthcare providers, and better time-management. Disadvantages found included distraction of nurses at work, and the perceived appearance of unprofessionalism. We believe there is an unmeasured risk of smartphones as potential vectors of infection. We support the use of smartphones to aid in patient care but recommend that education is necessary on the appropriate use of smartphones to mitigate risks such as infection, distraction, and accountability of personal use.
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How this classification was reachedexpand
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".