Online Machine Translation Use With Nursing Literature

Anazawa, Ryoko; Ishikawa, Hirono; Park, M J; Komamizu, Takahiro

doi:10.1097/nxn.0b013e3182701056

Cited by 11 publications

(24 citation statements)

References 7 publications

(6 reference statements)

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“…The tested MT software tools were either freely available on the web from commercial technology vendors or were in-house built systems created by the research teams themselves ( Multimedia Appendix 5 ). Regarding commercial vendors, Google Translate was the most used translation engine (28/46, 61%) [ 29 , 43 - 45 , 48 , 50 - 52 , 54 - 59 , 62 , 63 , 66 , 69 , 72 , 74 , 76 - 83 , 86 ], followed by Microsoft Bing (5/46, 11%) [ 44 , 47 , 61 , 69 , 79 ] and DeepL Translator (2/46, 4%) [ 73 , 86 ], among others. All these systems were used as domain-agnostic systems and not pretrained on specific language corpora.…”

Section: Resultsmentioning

confidence: 99%

“…Of the 46 articles, 40 (87%) [ 29 , 42 - 48 , 50 - 63 , 66 - 69 , 71 - 74 , 77 , 79 - 86 ] studied the use of MT to translate from 1 source language into 1 or several target languages ( Multimedia Appendix 5 ). Of these 40 articles, 37 (93%) [ 29 , 42 - 45 , 48 , 50 - 63 , 66 - 69 , 71 - 74 , 77 , 79 - 85 ] specified the source language, whereas 35 (88%) [ 29 , 42 - 45 , 48 , 50 - 52 , 54 - 63 , 66 , 67 , 69 , 71 - 74 , 77 , 79 - 86 ] specified the target language. Of the 12 source languages, English was the most commonly evaluated (32/37, 86%) [ 29 , 42 - 45 , 48 , 50 - 60 , 62 , 66 - 68 , 71 , 72 , 74 , 77 , 79 - 85 ], followed by French (4/37, 11%) [ 48 , 69 , 79 , 80 , 84 ], German (3/37, 8%) [ 69 ,…”

Section: Resultsmentioning

confidence: 99%

“…Of these 40 articles, 37 (93%) [ 29 , 42 - 45 , 48 , 50 - 63 , 66 - 69 , 71 - 74 , 77 , 79 - 85 ] specified the source language, whereas 35 (88%) [ 29 , 42 - 45 , 48 , 50 - 52 , 54 - 63 , 66 , 67 , 69 , 71 - 74 , 77 , 79 - 86 ] specified the target language. Of the 12 source languages, English was the most commonly evaluated (32/37, 86%) [ 29 , 42 - 45 , 48 , 50 - 60 , 62 , 66 - 68 , 71 , 72 , 74 , 77 , 79 - 85 ], followed by French (4/37, 11%) [ 48 , 69 , 79 , 80 , 84 ], German (3/37, 8%) [ 69 , 79 , 80 ], and Spanish (3/37, 8%) [ 79 , 80 , 84 ]. MT was tested in translating texts from English into at least 44 other languages, with Spanish (17/35, 49%) [ 29 , 48 , 50 - 52 , 57 - 60 , 66 , 72 , 74 ...…”

Section: Resultsmentioning

confidence: 99%

“…MT was tested in translating texts from English into at least 44 other languages, with Spanish (17/35, 49%) [ 29 , 48 , 50 - 52 , 57 - 60 , 66 , 72 , 74 , 79 , 80 , 83 - 85 ], Chinese (13/35, 37%) [ 50 - 52 , 56 , 58 , 62 , 72 , 74 , 77 , 81 - 83 , 85 ], and French (7/35, 20%) [ 52 , 71 , 74 , 79 , 80 , 83 , 84 ] being the most frequent target languages. Within this subset of 35 articles, 25 (71%) [ 29 , 42 - 45 , 50 - 52 , 54 , 56 , 57 , 59 - 63 , 66 , 69 , 72 - 74 , 77 , 79 , 80 , 85 ] conducted studies in specific geographies targeting populations and communities with limited language proficiency. Of these 25 articles, 19 (76%) [ 29 , 50 - 52 , 56 , 57 , 59 - 63 , 66 , 69 , 72 , 74 , 77 , 79 , 80 , ...…”

Section: Resultsmentioning

confidence: 99%

“…The monitoring studies adopted standard MT evaluation methods to measure the quality of MT output across various samples of health information material. Most of these studies focused on studying MT quality in terms of structural accuracy (28/34, 82%) [ 29 , 43 , 45 , 46 , 48 , 50 - 52 , 54 - 58 , 61 , 63 , 68 , 69 , 72 - 74 , 77 , 79 - 84 , 86 ] and fluency in unsupervised MT procedures (17/34, 50%) [ 44 - 46 , 50 , 51 , 54 , 56 , 57 , 59 , 62 , 66 , 71 , 72 , 77 , 80 , 81 , 85 ]. A quarter (8/28, 29%) of the articles [ 61 , 62 , 69 , 71 , 79 , 80 , 82 , 84 ] assessing structural accuracy supplemented their findings with standard automatic evaluation methods to verify the quality of MT output in comparison with the output of professional human translators.…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review

Herrera-Espejel,

Rach

2023

JMIR Public Health Surveill

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Background Culturally and linguistically diverse groups are often underrepresented in population-based research and surveillance efforts, leading to biased study results and limited generalizability. These groups, often termed “hard-to-reach,” commonly encounter language barriers in the public health (PH) outreach material and information campaigns, reducing their involvement with the information. As a result, these groups are challenged by 2 effects: the medical and health knowledge is less tailored to their needs, and at the same time, it is less accessible for to them. Modern machine translation (MT) tools might offer a cost-effective solution to PH material language accessibility problems. Objective This scoping review aims to systematically investigate current use cases of MT specific to the fields of PH and epidemiology, with a particular interest in its use for population-based recruitment methods. Methods PubMed, PubMed Central, Scopus, ACM Digital Library, and IEEE Xplore were searched to identify articles reporting on the use of MT in PH and epidemiological research for this PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews)–compliant scoping review. Information on communication scenarios, study designs and the principal findings of each article were mapped according to a settings approach, the World Health Organization monitoring and evaluation framework and the service readiness level framework, respectively. Results Of the 7186 articles identified, 46 (0.64%) were included in this review, with the earliest study dating from 2009. Most of the studies (17/46, 37%) discussed the application of MT to existing PH materials, limited to one-way communication between PH officials and addressed audiences. No specific article investigated the use of MT for recruiting linguistically diverse participants to population-based studies. Regarding study designs, nearly three-quarters (34/46, 74%) of the articles provided technical assessments of MT from 1 language (mainly English) to a few others (eg, Spanish, Chinese, or French). Only a few (12/46, 26%) explored end-user attitudes (mainly of PH employees), whereas none examined the legal or ethical implications of using MT. The experiments primarily involved PH experts with language proficiencies. Overall, more than half (38/70, 54% statements) of the summarizing results presented mixed and inconclusive views on the technical readiness of MT for PH information. Conclusions Using MT in epidemiology and PH can enhance outreach to linguistically diverse populations. The translation quality of current commercial MT solutions (eg, Google Translate and DeepL Translator) is sufficient if postediting is a mandatory step in the translation workflow. Postediting of legally or ethically sensitive material requires staff with adequate content knowledge in addition to sufficient language skills. Unsupervised MT is generally not recommended. Research on whether machine-translated texts are received differently by addressees is lacking, as well as research on MT in communication scenarios that warrant a response from the addressees.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review

Herrera-Espejel,

Rach

2023

JMIR Public Health Surveill

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Improve nursing in evidence-based practice: How Chinese nurses' read and comprehend scientific literature

Huang

Zhang²,

Han

et al. 2017

International Journal of Nursing Sciences

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Objectives To examine Chinese nurses' practice of reading and understanding scientific literature and elucidate the motivating and deterring factors. Methods This is a cross-sectional study conducted between March and June 2015. A random sample of 853 full-time registered nurses from three tertiary and two secondary hospitals in China filled out a set of self-administered questionnaires, including literature habit questionnaire(41 items), the situational motivation scale and the socio-demographic and professional characteristics questionnaire(15 items). Results Significant majority (89.2%) of the respondents reported perception of barriers to keeping up to date with literature. The language barrier was the most prominent, followed by poor presentation and readability of articles. Using simpler language when writing articles, improving ones' foreign language proficiency and getting education or training on nursing research were raised as the top facilitators. Additionally, reading and understanding literature was significantly associated with the nurses' educational background, motivation, genders and work settings. Conclusions The survey of the current status of literature education among Chinese nurses suggests that providing protected time, training for critical thinking, and incentive mechanisms will help improve nurses' engagement in literature and create a culture of academic inquiry.

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Scientific research driven by large-scale infrastructure projects: A case study of the Three Gorges Project in China

Jiang

Qiang

Fan

et al. 2018

Technological Forecasting and Social Change

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Online Machine Translation Use With Nursing Literature

Cited by 11 publications

References 7 publications

The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review

The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review

Improve nursing in evidence-based practice: How Chinese nurses' read and comprehend scientific literature

Scientific research driven by large-scale infrastructure projects: A case study of the Three Gorges Project in China

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