{"id":"W2799766028","doi":"10.2196/mental.9946","title":"Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data","year":2018,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mental health; Helpline; Psychology; Medicine; Computer science; Psychiatry; Data science; Emergency medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001260809,0.0003396479,0.0003085357,0.001938651,0.0002259715,0.0009005384,0.0003723394,0.0004008216,0.001089738],"category_scores_gemma":[0.009108747,0.0001116147,0.0004162674,0.0011716,0.0002062161,0.0003737586,0.0003167635,0.000556686,0.0005687372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006618952,"about_ca_system_score_gemma":0.0005833925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01158217,"about_ca_topic_score_gemma":0.01536027,"domain_scores_codex":[0.9992747,0.0002380952,0.00008761881,0.000112163,0.0002018178,0.00008559924],"domain_scores_gemma":[0.9923038,0.004725038,0.001362249,0.0003872656,0.0009700905,0.0002515565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002199796,0.0002080025,0.9712858,0.0000538254,0.00005927348,0.0001170145,0.0005486843,0.004500423,0.001418012,0.00003718065,0.0006313896,0.02092038],"study_design_scores_gemma":[0.000008005039,0.00025154,0.9500588,0.00002946672,0.00003526055,0.0001778853,0.001169835,0.04602856,0.001345211,0.0001183934,0.000750631,0.00002631227],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964928,0.000030784,0.001781413,0.0000721807,0.000005332948,0.00004556197,0.001018656,0.000069972,0.0004833765],"genre_scores_gemma":[0.9956072,0.00003385722,0.002179937,0.00001763569,0.000006457193,0.00003667495,0.001828918,0.000008157909,0.0002812179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01158217,"threshold_uncertainty_score":0.02302951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0505610052337384,"score_gpt":0.4402302504606658,"score_spread":0.3896692452269274,"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."}}