{"id":"W4380373444","doi":"10.1016/j.cmpb.2023.107645","title":"Development of a data-driven digital phenotype profile of distress experience of healthcare workers during COVID-19 pandemic","year":2023,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; St. Michael's Hospital; Ontario Tech University; Toronto Metropolitan University","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Support vector machine; Machine learning; Computer science; Artificial intelligence; Health care; Distress; Feature selection; Data mining; Medicine; Psychology; Clinical psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001485454,0.0005738034,0.0004576371,0.001433024,0.0001533732,0.0009032945,0.0007154288,0.0005590274,0.001326498],"category_scores_gemma":[0.006999876,0.0001638692,0.0004305315,0.0005712601,0.0001386799,0.0004999695,0.0008412444,0.0005570918,0.0006377368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799298,"about_ca_system_score_gemma":0.0005276285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002390182,"about_ca_topic_score_gemma":0.005271008,"domain_scores_codex":[0.999239,0.0002776058,0.0000862403,0.0001848024,0.0001542115,0.00005812433],"domain_scores_gemma":[0.997838,0.0007155293,0.0004255821,0.0001890822,0.0006371534,0.0001947618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001817938,0.0009904355,0.4907473,0.001034859,0.000485982,0.0005529564,0.001112345,0.02285256,0.01830621,0.001109187,0.03113449,0.4298558],"study_design_scores_gemma":[0.000214099,0.001941385,0.6891058,0.0003770002,0.0002214018,0.0007867684,0.00208381,0.2631504,0.01851024,0.003602846,0.01982988,0.0001763695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.765104,0.0003826327,0.1596723,0.001115818,0.0002024899,0.002387906,0.06082087,0.006112962,0.004201035],"genre_scores_gemma":[0.8187297,0.0002076878,0.124365,0.0003110374,0.00006864602,0.003052209,0.05188059,0.0001188339,0.001266223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002390182,"threshold_uncertainty_score":0.007855892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2540949874956108,"score_gpt":0.4827058831657219,"score_spread":0.2286108956701111,"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."}}