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241 Comparing the acceptability and feasibility of methods for translating decision support tools
  1. Danielle Schubbe1,
  2. Hannah Leavitt1,
  3. Renata W Yen1,2,
  4. Rachel C Forcino3,
  5. Saha Naseri4,
  6. Anji Zhu1,
  7. Aman Sinha1,
  8. Laila Serraj1,
  9. Marie-Anne Durand1,5,6
  1. 1The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
  2. 2Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
  3. 3Department of Population Health, University of Kansas School of Medicine, Kansas City, KS, USA
  4. 4Département de Biologie et Géosciences, Université Toulouse III Paul Sabatier, Toulouse, France
  5. 5Centre Universitaire de Médecine Générale Et Santé Publique, Unisanté, Rue du Bugnon 44, CH-1011, Lausanne, Switzerland
  6. 6UMR 1295, CERPOP, Université de Toulouse, Université Toulouse III Paul Sabatier, Toulouse, France


Introduction This study aims to compare the acceptability and feasibility of three translation methods - machine-generated (Google Translate), artificial intelligence-generated (ChatGPT), and a modified gold standard approach (TRAPD; Translation, Review, Adjudication, Pretest, and Documentation). We used these methods on a previously tested breast cancer conversation aid as a model tool for translation.

Methods Following the COnsolidated criteria for REporting Qualitative research checklist (CORE-Q), we conducted a qualitative study. We translated the breast cancer conversation aid into six languages (Arabic, French, Hebrew, Hindi, Persian, and Mandarin Chinese) employing the Google Translate, ChatGPT, and TRAPD methods. In the TRAPD method, two accredited translators independently translated the content, and team members reconciled any disparities. The study involved bilingual or trilingual team members with relevant qualifications and language expertise. Using an interview guide aimed at targeting the understandability and acceptability of each translation method (including ranking the translations), we are conducting cognitive debriefing interviews with a minimum of 18 bi-lingual participants. We are conducting thematic analysis to identify themes related to acceptability.

Results So far, six cognitive debriefing interviews for three languages have been conducted. Additional interviews are underway. Final results will be available in May 2024. Results will be narratively presented, emphasizing the effort and resources required for each translation method, offering insights into their acceptability and real-world feasibility.

Discussion This study contributes valuable evidence to guide effective and resource-conscious translation practices across diverse linguistic landscapes.

Conclusion Employing various translation methods for translation of a breast cancer conversation aid addresses a critical gap by comparing the acceptability and feasibility of these approaches across six languages. The findings aim to provide valuable insights into translation practices, contributing to the enhancement of translation accessibility and recommendations for use of certain methods and allocation of resources in diverse healthcare contexts.

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