{"id":"W2895995041","doi":"10.2196/12106","title":"An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":971,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mental health; mHealth; Psychology; Psychosocial; Population; Government (linguistics); Mobile phone; Psychological intervention; Internet privacy; Applied psychology; Medicine; Psychiatry; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02933564,0.001050547,0.001566256,0.001113195,0.00147148,0.002027484,0.001562053,0.001669941,0.003275348],"category_scores_gemma":[0.04311263,0.0005846039,0.002790883,0.0007922661,0.001115364,0.001579164,0.001653767,0.001828728,0.0008545617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001697604,"about_ca_system_score_gemma":0.002569328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002383458,"about_ca_topic_score_gemma":0.004608258,"domain_scores_codex":[0.982789,0.01301627,0.001446979,0.001056658,0.001239482,0.0004515873],"domain_scores_gemma":[0.9534011,0.03156312,0.003664775,0.003134365,0.00690975,0.001326967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.07142435,0.1722925,0.1568971,0.03214158,0.01145091,0.001521413,0.03345326,0.01049914,0.009039717,0.00469828,0.01785487,0.478727],"study_design_scores_gemma":[0.04390937,0.376541,0.3740039,0.01028703,0.01754669,0.001659913,0.03703905,0.05438285,0.01526562,0.00805972,0.06005326,0.001251526],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9351736,0.002446528,0.01649354,0.0005149869,0.0001833605,0.0393746,0.002944026,0.0001739436,0.002695354],"genre_scores_gemma":[0.7933578,0.001724909,0.07284536,0.001348394,0.0001894365,0.1248575,0.003544422,0.00006630173,0.002066018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02933564,"threshold_uncertainty_score":0.1551436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2590682131492887,"score_gpt":0.5721715764359566,"score_spread":0.3131033632866679,"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."}}