Usage of Multilingual Mobile Translation Applications in Clinical Settings
Bibliographic record
Abstract
BACKGROUND: Communication between patients and medical staff can be challenging if both parties have different cultural and linguistic backgrounds. Specialized applications can potentially alleviate these problems and significantly contribute to an effective, improved care process when foreign language patients are involved. OBJECTIVE: The objective for this paper was to discuss the experiences gained from a study carried out at the Hannover Medical School regarding the use of a mobile translation application in hospital wards. The conditions for successfully integrating these technologies in the care process are discussed. METHODS: iPads with a preinstalled copy of an exemplary multilingual assistance tool ("xprompt") designed for use in medical care were deployed on 10 wards. Over a period of 6 weeks, approximately 160 employees of the care staff had the opportunity to gather experiences with the devices while putting them to use during their work. Afterwards, the participants were asked to fill out an anonymous, paper-based questionnaire (17 questions) covering the usability of the iPads, translation apps in general, and the exemplary chosen application specifically. For questions requiring a rating, Likert scales were employed. The retained data were entered into an electronic survey system and exported to Microsoft Excel 2007 for further descriptive analysis. RESULTS: Of 160 possible participants, 42 returned the questionnaire and 39 completed the questions concerning the chosen app. The demographic data acquired via the questionnaire (ie, age, professional experience, gender) corresponded to the values for the entire care staff at the Hannover Medical School. Most respondents (35/39, 90%) had no previous experience with an iPad. On a 7-point scale, the participants generally rated mobile translation tools as helpful for communicating with foreign language patients (36/39, 92%; median=5, IQR=2). They were less enthusiastic about xprompt's practical use (36/39, median=4, IQR=2.5), although the app was perceived as easy-to-use (36/39, median=6, IQR=3) and there were no obvious problems with the usability of the device (36/39, median=6, IQR=2). CONCLUSIONS: The discrepancy between the expert ratings for xprompt (collected from the App Store and online) and the opinions of the study's participants can probably be explained by the differing approaches of the two user groups. The experts had clear expectations, whereas, without a more thorough introduction, our study participants perceived using the app as too time consuming in relation to the expected benefit. The introduction of such tools in today's busy care settings should therefore be more carefully planned to heighten acceptance of new tools. Still, the low return rate of the questionnaires only allows for speculations on the data, and further research is necessary. TRIAL REGISTRATION: This study was approved by the local institutional review board (IRB), Trial ID number: 1145-2011.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".