{"id":"W4323347946","doi":"10.1111/add.16179","title":"Using machine learning to retrospectively predict self‐reported gambling problems in Quebec","year":2023,"lang":"en","type":"article","venue":"Addiction","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Social Sciences and Humanities Research Council of Canada; Concordia University","keywords":"Psychology; Random forest; Harm; Logistic regression; Artificial intelligence; Demographics; Machine learning; Clinical psychology; Demography; Social psychology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003313278,0.0001527284,0.0001722333,0.000526628,0.000139751,0.00003843653,0.0000640225,0.000122268,0.0002002806],"category_scores_gemma":[0.00005348721,0.0001596362,0.00005331083,0.001231601,0.00001204783,0.0001285625,0.00003641791,0.0002768005,0.0003511707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003379231,"about_ca_system_score_gemma":0.00002985621,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03925988,"about_ca_topic_score_gemma":0.006129429,"domain_scores_codex":[0.9985971,0.0000989477,0.0003111929,0.000428218,0.000206867,0.0003576378],"domain_scores_gemma":[0.9995205,0.00003370778,0.0001111692,0.0001996836,0.00005277435,0.00008210702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004145532,0.0002028142,0.9833793,0.00000468332,0.0001285645,0.0001317575,0.004571359,0.003347089,0.003995105,0.00003542268,0.0001618938,0.004000561],"study_design_scores_gemma":[0.001052265,0.0001834349,0.9920238,0.00006166319,0.00009894735,0.00003033582,0.0002924613,0.004532921,0.0002025118,0.00003204534,0.001337433,0.0001522204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962494,0.00004841171,0.0003019229,0.00004338854,0.0007675534,0.0004073753,0.00001291059,0.0008037637,0.001365323],"genre_scores_gemma":[0.9961839,0.000008038308,0.0002667079,0.00001604811,0.00008552182,0.00009487345,0.000112463,0.00004270582,0.003189766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03313045,"threshold_uncertainty_score":0.9671378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1291482630581393,"score_gpt":0.3889816561710976,"score_spread":0.2598333931129583,"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."}}