{"id":"W3213369726","doi":"10.3389/fpsyt.2021.734909","title":"Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care","year":2021,"lang":"en","type":"article","venue":"Frontiers in Psychiatry","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Centre for Addiction and Mental Health","funders":"National Institute of Mental Health; National Institute on Aging; University of Washington","keywords":"Mental health; Perspective (graphical); Health care; Clinical Practice; Mental health care; Psychology; Medicine; Data science; Nursing; Psychiatry; Artificial intelligence; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06396858,0.0009432929,0.001439854,0.004565726,0.0109912,0.02426391,0.004783864,0.01593279,0.004510478],"category_scores_gemma":[0.04777115,0.0006469301,0.00106591,0.00330309,0.05727214,0.01857255,0.01826982,0.02066878,0.0008674267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0122466,"about_ca_system_score_gemma":0.02459776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00542375,"about_ca_topic_score_gemma":0.005219841,"domain_scores_codex":[0.9501166,0.04168062,0.001319463,0.001651282,0.00297603,0.002255978],"domain_scores_gemma":[0.9184538,0.06350803,0.001897422,0.001881008,0.005426329,0.008833356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004343771,0.0001391655,0.001005684,0.0009265894,0.00005730618,0.0009805148,0.04426052,0.0009394432,0.0001472793,0.8706791,0.03348717,0.0473338],"study_design_scores_gemma":[0.00002514825,0.00008293919,0.0005328632,0.002269859,0.00001959559,0.0006856587,0.03806563,0.000766947,0.00007960317,0.8278899,0.1295338,0.00004817593],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.002321942,0.04042775,0.01305991,0.9217919,0.001996943,0.00005565181,0.00002031604,0.00002369326,0.02030188],"genre_scores_gemma":[0.4866526,0.1036159,0.04566068,0.3437071,0.01140078,0.0009275914,0.0000785763,0.0001052085,0.007851612],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.06396858,"threshold_uncertainty_score":0.3383023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.200752898087192,"score_gpt":0.528149906526677,"score_spread":0.327397008439485,"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."}}