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Using the Novel Translation Validity Index to Evaluate GenAI Translations of the Patient Health Questionnaire-9 Into
Ratchneewan Ross1, Lenny Chiang-Hanisko2, Said Abusalem3
1University of Louisville School of Nursing, Louisville, KY, USA ratchneewan.ross@louisville.edu.
None:
Background and Purpose: Depressive symptoms affect 280 million people worldwide. Although generative artificial intelligence (GenAI) tools are increasingly used in health care translation, their translation performance across languages remains unclear. To our knowledge, no structured tool exists for evaluating the quality of language translation. This study aimed to create a translation validity index (TVI) and examine the Patient Health Questionnaire-9's translation quality produced by three GenAI platforms across nine languages using TVI. Methods: Two bilingual evaluators per language (N = 18) assessed translations by chat generative pretrained transformer (ChatGPT), Copilot, and Google Translate using TVI criteria of cultural appropriateness, grammar, and semantic clarity. Scores ≥3 indicated acceptable quality. Results: ChatGPT and Copilot achieved higher TVI scores (3.11-3.66) in high- and medium-resource languages, while Google Translate met acceptability for Ewe (3.73). Conclusions: TVI is novel and is potentially useful for evaluating forward and backward language translation quality. Human expert input remains essential for accurate mental health measure development.
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