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Record W3008861094 · doi:10.3390/ijerph17041368

A Digital Communication Assistance Tool (DCAT) to Obtain Medical History from Foreign-Language Patients: Development and Pilot Testing in a Primary Health Care Center for Refugees

2020· article· en· W3008861094 on OpenAlexaff
Frank Müller, Shivani Chandra, Ghefar Furaijat, Stefan Kruse, Alexandra Waligorski, Anne Simmenroth, Evelyn Kleinert

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsWomen's College Hospital
FundersEuropean Social FundOrder of MaltaRobert Bosch Stiftung
KeywordsUsabilityMultidisciplinary approachInterpreterRefugeeHealth careDigital healthLanguage barrierHealth literacyNursingMedicineComputer scienceMedical educationPsychologyPolitical scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

Background: Language barriers play a critical role in the treatment of migrant and refugee patients. In Germany, primary care interpreters are often not available especially in rural areas or if patients demand spontaneous or urgent consultations. Methods: In order to enable patients and their physicians to communicate effectively about the current illness history, we developed a digital communication assistance tool (DCAT) for 19 different languages and dialects. This paper reports the multidisciplinary process of the conceptual design and the iterative development of this cross-cultural user-centered application in an action-oriented approach. Results: We piloted our app with 36 refugee patients prior to a clinical study and used the results for further development. The acceptance and usability of the app by patients was high. Conclusion: Using digital tools for overcoming language barriers can be a feasible approach when providing health care to foreign-language patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.438
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2020
Admission routes1
Has abstractyes

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