{"id":"W4304890991","doi":"10.3389/fdgth.2022.877762","title":"Digital phenotyping for classification of anxiety severity during COVID-19","year":2022,"lang":"en","type":"article","venue":"Frontiers in Digital Health","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anxiety; Support vector machine; Coronavirus disease 2019 (COVID-19); Proxy (statistics); Artificial intelligence; Survey data collection; Psychology; Computer science; Generalized anxiety disorder; Population; Psychological intervention; Machine learning; Medicine; Clinical psychology; Psychiatry; Statistics; Mathematics; Environmental health; Disease","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007007662,0.0003644021,0.0003039136,0.002978939,0.0005411794,0.0008330283,0.000604305,0.000412428,0.002422607],"category_scores_gemma":[0.00499975,0.000133669,0.0003954302,0.002102361,0.0002410011,0.0002532138,0.0007601996,0.0004302535,0.001129427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001882808,"about_ca_system_score_gemma":0.001800612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2879314,"about_ca_topic_score_gemma":0.3778466,"domain_scores_codex":[0.9992296,0.00009518559,0.00005016962,0.0001297276,0.0003024496,0.0001929827],"domain_scores_gemma":[0.9975433,0.0003627834,0.0003995191,0.0002091224,0.001232791,0.0002523874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004342012,0.0001403482,0.8560846,0.0001690538,0.00007308895,0.0002098206,0.0008421674,0.001795609,0.004164729,0.0004614665,0.01441809,0.1212069],"study_design_scores_gemma":[0.00000825554,0.00005397692,0.9823937,0.00003785731,0.00002475919,0.0001342775,0.0007474786,0.009204326,0.001469875,0.0001521646,0.005751648,0.00002165689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9169904,0.0004998601,0.01378577,0.000584365,0.00008694284,0.0007000519,0.05602064,0.001128663,0.0102033],"genre_scores_gemma":[0.9408579,0.0003222095,0.01808671,0.0001339487,0.00003384624,0.0004322595,0.03698848,0.00005152226,0.003093107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2879314,"threshold_uncertainty_score":0.572511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04258044208638447,"score_gpt":0.3707336661209212,"score_spread":0.3281532240345367,"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."}}