ArticleNursing open2015
A prospective observational study of machine translation software to overcome the challenge of including ethnic diversity in healthcare research.
Article in Nursing open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Machine translation: Turkish-English bilingual speakers' accuracy detection of evidentiality and preference of MT.Cognitive research: principles and implications · 2024Article
- The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review.JMIR public health and surveillance · 2023Article
- Is machine translation a dim technology for its users? An eye tracking study.Frontiers in psychology · 2023Article
- Development and validation of a multi-lingual online questionnaire for surveying the COVID-19 prevention and control measures used in global workplaces.BMC public health · 2022Article
- Evaluation of a Language Translation App in an Undergraduate Medical Communication Course: Proof-of-Concept and Usability Study.JMIR mHealth and uHealth · 2021Article
- Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation.International journal of environmental research and public health · 2021Article
- Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations.Computational intelligence and neuroscience · 2021Article
- Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders: Developing Interpretable Bayesian Machine Learning Classifiers.Frontiers in psychiatry · 2021Article
- Evaluating the Usefulness of Translation Technologies for Emergency Response Communication: A Scenario-Based Study.JMIR public health and surveillance · 2019Article
- What's different aboutNursing open · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThis study investigates whether machine translation could help with the challenge of enabling the inclusion of ethnic diversity in healthcare research.
designA two phase, prospective observational study.
methodsTwo machine translators, Google Translate and Babylon 9, were tested. Translation of the Strengths and Difficulties Questionnaire (SDQ) from 24 languages into English and translation of an English information sheet into Spanish and Chinese were quality scored. Quality was assessed using the Translation Assessment Quality Tool.
resultsOnly six of the 48 translations of the SDQ were rated as acceptable, all from Google Translate. The mean number of acceptably translated sentences was higher (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.