Evaluation of a Hands-Free Communication Device in an Acute Care Setting
Bibliographic record
Abstract
Quality medical care hinges on healthcare providers being able to communicate effectively and efficiently. In this study, we examine if healthcare providers' perceptions of the performance of a wireless communication device are consistent with what it is claimed the technology can offer, namely, improved patient safety and quality of care. We used a mixed-methods design where we collected data from a single medical unit. During the qualitative component of the study, we conducted face-to-face interviews to explore healthcare team members' perceptions of the impact of a wireless communication device on their day-to-day patient care activities. Three major improvements were identified from the interview data: more direct and effective communication, improved work efficiency, and enhanced continuity of patient care. The quantitative component consisted of a questionnaire constructed from the major themes extracted from the interviews. Many of the healthcare team members reported that the wireless communication device improved their communication and allowed them to complete their work more efficiently. In addition, the questionnaire findings suggest that both improved communication and work efficiency are correlated with perceptions of improved quality of patient care. Based on the results of this study, this wireless communication device does live up to its aims of enhancing communication, staff efficiency, and improving perceived patient safety.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".