Smartphone Technology: Enabling Prioritization of Patient Needs and Enhancing the Nurse-Patient Relationship
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) is a rapidly growing field with the potential to transform healthcare delivery. Smartphone technologies have been developed and integrated into the patient call bell system for healthcare staff to receive calls; however, there is a lack of high-quality evidence to support the implementation and evaluate the effectiveness of these devices in a healthcare setting. AIM: The aim of this study is to explore nurses' perceptions of smartphone technology devices in enhancing the nurse-patient relationship and improving nursing workflows. METHODS: A semi-structured focus group and interviews were used to illicit nurses' experiences with smartphone technology. Interviews were audio recorded, transcribed and subjected to a content analysis to identify emerging themes from the data. RESULTS: Interviews with nurses provided insight into the benefits and challenges of smartphone use in the clinical setting. Multiple benefits were identified by nurse participants, including time management and convenience, prioritization, patient safety and enhancement of the nurse-patient relationship. CONCLUSION: There are multiple benefits of smartphone technology for both nurses and patients. Hospitals proposing to introduce smartphone technology need to educate patients and families about the clinical use of smartphones to avoid unfavourable perceptions. Smartphone technology must be interoperable with the electronic medical record to optimize interprofessional communication and exchange of patient information.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".