Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14123/1742
Original TitleSupplementary Material for the Paper "Opening the black box - Effects of decision transparency on therapists' trust in and intended use of AI-based decision support systems in ICBTs"
Handle20.500.14123/1742
Kinds of DataTest Data (i.e. Educational, Psychical or Psychological Measurements)
Context Materials / Supporting information
Resource TypeDataset
CreatorZantvoort, Kirsten  0000-0001-9876-054X (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16)
Bjurner, Pontus  0000-0002-4967-9128 (Karolinska Institutet  056d84691)
Forsell, Erik  0000-0001-8236-4323 (Karolinska Institutet  056d84691)
Wallert, John  0000-0002-1473-4916 (Karolinska Institutet  056d84691)
Funk, Burkhardt  0000-0001-5855-2666 (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16)
Kaldo, Viktor  0000-0002-6443-5279 (Karolinska Institutet  056d84691)
Description of the DatasetThis study investigated the impact of local SHAP (SHapley Additive exPlanation) values on therapists’ perceptions of a Machine Learning-based Decision Support Tools (DST) in Internet-based Cognitive Behavioural Therapy (ICBT). The randomised experiment included 35 Swedish ICBT therapists who were each presented with a DST with six exemplary patient cases with SHAP values and six without them. The study measured therapists' understanding, trust, and perception of clinical usefulness through self-ratings at multiple stages. The primary hypothesis that adding SHAP values increased self-reported trust was confirmed by the findings (p=0.01, d=0.43 [0.07-0.76]). Further, the results suggested increased understanding, agreeance with, and the perceived usefulness of the DST predictions through SHAP values.
MethodsExperiment (Web-based)
Description
Programming / Script-based data collection
KeywordsMaschinelles Lernen; Data Science; Therapeutik; Kognitives Verhalten; Verhaltenstherapie; Entscheidungsfindung; Internetbasiert; Digitale Gesundheit; Mentale Gesundheit; Intervention; Machine Learning; Data Science; Therapeutics; Cognitive Behavior; Behavioral Therapy; Decision Making; Internet-based; Digital Health; Mental Health; Intervention
Thematic ClassificationDigital Health
Published byMedien- und Informationszentrum, Leuphana Universität Lüneburg
Superordinate Data Collection Supplementary Material PhD Kirsten Zantvoort
Related Resources Relations of the dataset

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