Enabling Nurse-Patient Communication With a Mobile App: Controlled Pretest-Posttest Study With Nurses and Non–English-Speaking Patients
Bibliographic record
Abstract
Background There is growing concern regarding the implications of miscommunication in health care settings, the results of which can have serious detrimental impacts on patient safety and health outcomes. Effective communication between nurses and patients is integral in the delivery of timely, competent, and safe care. In a hospital environment where care is delivered 24 hours a day, interpreters are not always available. In 2014, we developed a communication app to support patients’ interactions with allied health clinicians when interpreters are not present. In 2017, we expanded this app to meet the needs of the nursing workforce. The app contains a fixed set of phrases translated into common languages, and communication is supported by text, images, audio content, and video content. Objective This study aims to evaluate the efficacy of the communication app to support nursing staff during the provision of standard care to patients from non–English-speaking backgrounds when an interpreter is not available. Methods This study used a one-group pretest-posttest sequential explanatory mixed methods research design, with quantitative data analyzed using inferential statistics and qualitative data analyzed via thematic content analysis. A total of 134 observation sessions (82 pretest and 52 posttest) of everyday nurse-patient interactions and 396 app use sessions were recorded. In addition, a total of 134 surveys (82 pretest and 52 posttest) with nursing staff, 7 interviews with patients, and 3 focus groups with a total of 9 nursing staff participants were held between January and November 2017. Results In the absence of the app, baseline interactions with patients from English-speaking backgrounds were rated as more successful (t80=5.69; P<.001) than interactions with patients from non–English-speaking backgrounds. When staff used the app during the live trial, interactions with patients from non–English-speaking backgrounds were rated as more successful than interactions without the app (F2,119=8.17; P<.001; η2=0.37). In addition, the level of staff frustration was rated lower when the app was used to communicate (t80=2.71; P=.008; r=0.29). Most participants indicated that the app assisted them in communicating. Conclusions Through the use of the app, a number of patients from non–English-speaking backgrounds experienced better provision of standard care, similar to their English-speaking peers. Thus, the app can be seen as contributing to the delivery of equitable health care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".