{"id":"W3160614900","doi":"10.1177/07067437211016255","title":"Reading Between the Lines: A Pursuit of Estimating the Population Prevalence of Mental Illness Using Multiple Data Sources","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Psychiatry","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Conceptualization; Context (archaeology); Mental illness; Population; Public health; Mental health; Population health; Resource (disambiguation); Computer science; Missing data; Psychology; Data science; Econometrics; Psychiatry; Medicine; Environmental health; Geography; Machine learning; Artificial intelligence; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2336146,0.003362346,0.005244411,0.02238468,0.009549174,0.03059065,0.01300407,0.008602517,0.003094743],"category_scores_gemma":[0.4222975,0.002314189,0.004299569,0.02620762,0.02448493,0.0413578,0.02299655,0.01946783,0.00119791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01471778,"about_ca_system_score_gemma":0.03615697,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09792491,"about_ca_topic_score_gemma":0.1004799,"domain_scores_codex":[0.7416247,0.1902769,0.01711668,0.01286438,0.03611011,0.00200719],"domain_scores_gemma":[0.5991133,0.2751171,0.02832737,0.04074471,0.05268308,0.004014419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000366749,0.0002200031,0.05706961,0.00510891,0.004475012,0.0009352116,0.05633484,0.005967708,0.002392208,0.4596329,0.0359438,0.371553],"study_design_scores_gemma":[0.0001696206,0.0004143297,0.0181135,0.009529447,0.001591838,0.0007812928,0.02707725,0.01225624,0.002877113,0.8161961,0.1103778,0.000615483],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01338648,0.008261181,0.8310542,0.1289721,0.003025941,0.001234145,0.001498544,0.0006771084,0.01189033],"genre_scores_gemma":[0.09033828,0.003891986,0.8895851,0.01137671,0.001495365,0.001018489,0.0007027931,0.0002857519,0.001305541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9020751,"threshold_uncertainty_score":0.9450896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07327715217316828,"score_gpt":0.3650121761343646,"score_spread":0.2917350239611963,"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."}}