{"id":"W2751538252","doi":"","title":"A Novel Measure of Work Stress: Identifying Work Stressor Patterns in Canada Using Latent Class Analysis","year":2017,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Workplace Health and Well-being","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent class model; Stressor; Work stress; Measure (data warehouse); Class (philosophy); Work (physics); Stress (linguistics); Psychology; Computer science; Data mining; Machine learning; Artificial intelligence; Engineering; Clinical psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001116088,0.0004618715,0.0003993343,0.003505189,0.003091485,0.002340061,0.001189551,0.0003310576,0.002948649],"category_scores_gemma":[0.003708052,0.0002196535,0.0008859358,0.00544509,0.0007623818,0.0006301993,0.002103181,0.0008264179,0.0002199014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01183757,"about_ca_system_score_gemma":0.01841064,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9493788,"about_ca_topic_score_gemma":0.9624889,"domain_scores_codex":[0.998827,0.0001604089,0.00007343187,0.0001371951,0.0004838478,0.0003180228],"domain_scores_gemma":[0.9982944,0.0001710274,0.0003537713,0.0001048019,0.0006862913,0.0003896689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009991846,0.0001009758,0.9786644,0.00003688404,0.0000887935,0.00003082233,0.001982725,0.000439793,0.0003091779,0.0007001108,0.001148411,0.01639794],"study_design_scores_gemma":[0.00000958464,0.00002756182,0.993693,0.0000195434,0.00002046984,0.00001676027,0.002365735,0.002222522,0.00007682003,0.0003732263,0.001158886,0.00001603378],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846026,0.0002292251,0.003214937,0.0002700466,0.00001954678,0.0002726295,0.005980944,0.00005304844,0.005357082],"genre_scores_gemma":[0.9928994,0.000138751,0.002472801,0.0000270825,0.000005876422,0.000173408,0.003049495,0.00001142968,0.001221605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05062115,"threshold_uncertainty_score":0.1018385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1927483398869278,"score_gpt":0.3954007284632746,"score_spread":0.2026523885763468,"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."}}