{"id":"W4212999936","doi":"10.2196/preprints.37142","title":"Exploring COVID-19–Related Stressors: Topic Modeling Study (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Mental Health via Writing","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Stressor; Mental health; Pandemic; Psychosocial; Latent Dirichlet allocation; Topic model; Coronavirus disease 2019 (COVID-19); Psychology; Social media; Set (abstract data type); Gerontology; Medicine; Psychiatry; Computer science; Artificial intelligence; World Wide Web; Disease","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":[0.003686429,0.0004212023,0.0004685714,0.00216404,0.0008652757,0.001881175,0.0006107259,0.0007565648,0.003156383],"category_scores_gemma":[0.01165168,0.0002578825,0.001499331,0.002480187,0.0003187243,0.001828426,0.000944894,0.001203513,0.001096172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009005397,"about_ca_system_score_gemma":0.0007800266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02151845,"about_ca_topic_score_gemma":0.02176241,"domain_scores_codex":[0.9990358,0.0004606401,0.00008658913,0.0002164238,0.0001151113,0.00008542483],"domain_scores_gemma":[0.9833511,0.01445879,0.0007588611,0.0004154066,0.0007274376,0.0002883118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002830544,0.0009964817,0.8891326,0.0005217475,0.0004117542,0.0003689695,0.01353103,0.005423735,0.00102912,0.002142556,0.02542343,0.06073544],"study_design_scores_gemma":[0.00005687143,0.0003060813,0.814781,0.0003448229,0.0003445158,0.0005141849,0.03196248,0.1289072,0.001168048,0.002322685,0.01916625,0.0001258769],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770033,0.0006436903,0.008751913,0.002293552,0.00009508451,0.0002659445,0.008574287,0.0001287366,0.002243466],"genre_scores_gemma":[0.9715484,0.0007599492,0.00909263,0.0002758897,0.0002189084,0.0004728815,0.01525814,0.00006174216,0.002311449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02151845,"threshold_uncertainty_score":0.04278636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4203252574320279,"score_gpt":0.4648278442745534,"score_spread":0.0445025868425255,"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."}}