Smartphone Technology: Impact on Interprofessional Working Relations between Doctors and Nurses
Bibliographic record
Abstract
BACKGROUND: For decades, the main communication technology in hospitals has been the paging system. In the era of digital communication, smartphones have been adopted by hospitals seeking to modernize processes and offer real-time, two-way communication to increase efficiency. OBJECTIVE: The aim of this study was to explore physicians' and nurses' perceptions of the impact of smartphones on communication and efficiency. METHODS: Mann-Whitney U-tests were used to compare differences in item scores between physicians and nurses on 17 questionnaire items relating to smartphone impact on interpersonal relationships and communication, efficiency and reliability. An open-ended question was used to elicit additional feedback. RESULTS: In total, 43 nurses and 27 physicians participated in the study. Nurses' ratings were significantly higher than physicians' on a number of questionnaire items, including the following: smartphones have a positive impact on efficiency (Mdn = 4.0 vs. 3.0, U = 321.0, p = 0.027, r = .33), smartphones increase my accessibility to physicians (Mdn = 5.0 vs. 3.0, U = 277.0, p = 0.009, r = 0.42) and smartphones reduce interruptions versus pagers (Mdn = 4.0 vs. 2.0, U = 224.0, p > 0.0001, r = 0.47). CONCLUSION: The findings suggest that smartphone technology may reduce the locus of control for physicians, potentially limiting their ability to prioritize patients' needs and manage workflow efficiently.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".