{"id":"W4399839403","doi":"10.1016/j.habitatint.2024.103119","title":"Faith, policy, and suicide: A Machine learning and spatial analysis approach of religious affiliation and suicide rates in Toronto","year":2024,"lang":"en","type":"article","venue":"Habitat International","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Faith; Suicide rates; Suicide prevention; Sociology; Criminology; Psychology; Poison control; Political science; Medical emergency; Medicine; Theology; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006064166,0.0002061233,0.000235969,0.001089047,0.001872776,0.001235028,0.0006735325,0.0003136245,0.002478255],"category_scores_gemma":[0.0043951,0.0003047161,0.0005505158,0.003149236,0.0008935662,0.0004147451,0.001291793,0.0007446709,0.0001257397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01107655,"about_ca_system_score_gemma":0.008477395,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9793125,"about_ca_topic_score_gemma":0.9883099,"domain_scores_codex":[0.9996279,0.00017452,0.00002734572,0.00004477541,0.00004639568,0.00007906289],"domain_scores_gemma":[0.9978821,0.0006928007,0.0004440466,0.0001241223,0.0003832363,0.0004736712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007029957,0.00003990355,0.9838266,0.00002812367,0.0001000822,0.0001565262,0.005184356,0.002388528,0.0001484745,0.001469499,0.0008824074,0.005705267],"study_design_scores_gemma":[0.000005712562,0.00002094508,0.9819081,0.00003004422,0.00004595097,0.00004815901,0.01119801,0.005637481,0.00007887385,0.000264982,0.0007481501,0.00001347202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980152,0.0001925051,0.000216365,0.0002740559,0.000003873859,0.000006552301,0.0005706769,0.000004140556,0.0007166911],"genre_scores_gemma":[0.9991443,0.0001272348,0.0001346351,0.000006018999,0.000001910037,0.000004040487,0.0001858476,0.000001658409,0.0003942574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02068746,"threshold_uncertainty_score":0.08036643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862426690380331,"score_gpt":0.3446956334953777,"score_spread":0.3260713665915744,"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."}}