{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008705707,0.0003323038,0.0002891559,0.001141414,0.0009499856,0.001014585,0.0009044084,0.0004180046,0.00297855],"category_scores_gemma":[0.004081521,0.000203075,0.0003834703,0.001473746,0.0004079902,0.0003654693,0.0004720202,0.0007060294,0.0003737851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01083428,"about_ca_system_score_gemma":0.006189572,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9827648,"about_ca_topic_score_gemma":0.9862017,"domain_scores_codex":[0.999594,0.00007785513,0.00002459092,0.00008876644,0.0001314168,0.0000833385],"domain_scores_gemma":[0.9973567,0.0004251264,0.0005639949,0.0001071372,0.001148613,0.0003984038],"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":[0.00005790852,0.00006907805,0.9884846,0.00001849166,0.00006001112,0.00004040846,0.0001890243,0.000815847,0.00009614622,0.00007280825,0.001655591,0.008440152],"study_design_scores_gemma":[0.00001301183,0.00004129137,0.9914284,0.00003137192,0.0000215428,0.00003138,0.0003795431,0.007132897,0.00006472206,0.00004527701,0.0007999085,0.00001061006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992856,0.0002472744,0.0005183314,0.0003534169,0.000009461684,0.00005621916,0.003981532,0.00003112122,0.001946619],"genre_scores_gemma":[0.9954239,0.0001395606,0.0004558119,0.00007570776,0.000004043733,0.00003121558,0.002729835,0.00000580768,0.001134237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01723522,"threshold_uncertainty_score":0.07860857,"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."}}