AI-assisted translation and psychometric evaluation of the Digital Competencies for Applied Practitioners Scale (DCAPP) in a sample of Swedish psychology master’s students (Preprint)

BACKGROUND As psychological practice becomes increasingly digitalized, the demand for competencies in digital psychology is growing. Although competency frameworks for digital clinical practice exists, validated instruments to assess these competencies remain scarce. In Sweden, psychology master’s students are now being offered courses in digital clinical psychology, increasing the need for instruments to measure intended improvements in knowledge and abilities. Using artificial intelligence (AI) to assist translation procedures can facilitate the adaptation of existing instruments to new national and cultural context. OBJECTIVE To test an AI-assisted procedure for the translation and contextual adaptation of the Digital Competencies for Applied Psychological Practitioner (DCAPP) scale to Swedish. To examine the psychometric properties of the translated version on a sample of psychology master’s students in Sweden, including pilot testing of the instruments responsiveness to change in knowledge and abilities among students attending a course in digital clinical psychology. METHODS An AI-assisted adaptation procedure, using ChatGPT and DeepL, was used to translate the DCAPP from English to Swedish. The Swedish version was distributed to psychology master’s students during their eighth semester, including those attending an elective course in digital clinical psychology. Twenty-four students completed the baseline measurement. Nine out of 14 students that attended the course also provided data at follow-up. Item descriptives, internal consistency and responsiveness to change were calculated for the scale. RESULTS The AI-assisted translation procedure resulted in a translated version of the scale with both high quality and semantic similarity ratings. The Swedish DCAPP demonstrated excellent internal consistency for total score (α = .96), and also for knowledge (α = .93) and ability (α = .96) subscales. It demonstrated acceptable item distributions with item-total correlation above .30 (range: 0.53-0.87) and mean-item correlation for subscales were acceptable but indicated potential item redundancy (Knowledge r=.48; Abilities r=.61). Whilst skewness and kurtosis values were mostly acceptable, high floor effects were observed in both subscales. A statistically significant increase in students’ competency ratings was observed at post-test (P