{"id":"W4398252103","doi":"10.1097/ncm.0000000000000740","title":"Developing and Deploying Emotional Intelligence to Uncover and Address Clients’ Needs","year":2024,"lang":"en","type":"article","venue":"Professional Case Management","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Capital Commission","funders":"","keywords":"Computer science; MEDLINE; Emotional intelligence; Psychology; Data science; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007568303,0.0002036048,0.0002203819,0.0004263807,0.001005028,0.00004219085,0.0001060598,0.0001078538,0.0003543098],"category_scores_gemma":[0.00004512048,0.0001663542,0.00003276276,0.0004319836,0.00003952844,0.0002065358,0.001058183,0.0004753449,0.0002430197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004457265,"about_ca_system_score_gemma":0.0003370547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008550004,"about_ca_topic_score_gemma":0.00009968404,"domain_scores_codex":[0.9980075,0.0002053791,0.0004965184,0.0004255952,0.0003488569,0.0005161334],"domain_scores_gemma":[0.9988952,0.0005454898,0.00005624872,0.0001733458,0.00007962558,0.0002501044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001675179,0.0000565908,0.0300237,0.009302001,0.0001800876,0.003286973,0.00698038,0.00001487186,0.00001476967,0.6567658,0.05962558,0.2335817],"study_design_scores_gemma":[0.0007696365,0.0001533147,0.0568548,0.00638514,0.0001712108,0.0005580618,0.03978683,0.0008023052,0.00004112275,0.0330526,0.8603594,0.001065573],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8436507,0.005059647,0.03287603,0.07752244,0.009845497,0.004498646,0.0001039024,0.0006474748,0.02579569],"genre_scores_gemma":[0.9176183,0.0007492286,0.01051807,0.05427193,0.0004943752,0.0006193353,0.00003883541,0.00006485054,0.01562509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8007338,"threshold_uncertainty_score":0.7729963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1052044115563902,"score_gpt":0.4564226780439419,"score_spread":0.3512182664875517,"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."}}