{"id":"W4415794686","doi":"10.1145/3772071","title":"Designing and Personalising Hybrid Health Explanations for Lay Users","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fonds Wetenschappelijk Onderzoek","keywords":"Modalities; Set (abstract data type); Modality (human–computer interaction); Preference; Coaching; Recommender system; Feature (linguistics); Design science; Preference elicitation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006530377,0.0002806395,0.0003549653,0.0006709271,0.0008125057,0.0004793131,0.0008003167,0.00006488048,0.00001526176],"category_scores_gemma":[0.0002231996,0.0002876412,0.0001574408,0.000518534,0.00007383039,0.0009448413,0.00002821053,0.0003105878,0.00003891152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005403052,"about_ca_system_score_gemma":0.0002013853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007304025,"about_ca_topic_score_gemma":0.0001046802,"domain_scores_codex":[0.9976964,0.0002364675,0.000631677,0.0007039818,0.0002562899,0.0004751986],"domain_scores_gemma":[0.9966716,0.001921594,0.0002116598,0.0007068298,0.0003372495,0.0001511233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005895295,0.001521118,0.000311425,0.0008388094,0.00167064,0.00004274749,0.03629135,0.08004063,0.008265722,0.2299333,0.00764874,0.632846],"study_design_scores_gemma":[0.0008889013,0.001635473,0.0001057203,0.003425614,0.0001246567,0.0001840849,0.05282929,0.3090056,0.5252269,0.01406732,0.09092562,0.001580898],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002617998,0.0003899587,0.9889596,0.00428284,0.002009791,0.001122165,0.00003958098,0.0002075409,0.0003705004],"genre_scores_gemma":[0.9699056,0.0001232519,0.02737574,0.0007962206,0.00005208766,0.0005070798,0.000008072915,0.00002440138,0.001207579],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9672875,"threshold_uncertainty_score":0.9999576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0489261400052198,"score_gpt":0.3378237363270465,"score_spread":0.2888975963218268,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}