{"id":"W3129384632","doi":"10.1287/inte.2022.1132","title":"Optimization Helps Scheduling Nursing Staff at the Long-Term Care Homes of the City of Toronto","year":2022,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Scheduling (production processes); Status quo; Computer science; Absenteeism; Schedule; Long-term care; Operations management; Operations research; Nursing; Nurse scheduling problem; Business; Job shop scheduling; Medicine; Flow shop scheduling; Economics; Engineering; Management","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":[0.0006522537,0.0006178925,0.0002448763,0.0005091851,0.0007546591,0.0008987538,0.0004172637,0.0002565569,0.004700556],"category_scores_gemma":[0.002325841,0.0002338699,0.0003024822,0.0007138348,0.0002238419,0.0003139021,0.0003056003,0.0003350389,0.0002967335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004430125,"about_ca_system_score_gemma":0.007034791,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3443633,"about_ca_topic_score_gemma":0.447951,"domain_scores_codex":[0.9996314,0.0001236182,0.00002041434,0.00006419654,0.00008789384,0.00007246379],"domain_scores_gemma":[0.9989733,0.0005978208,0.00009221824,0.00003007418,0.0002099339,0.00009676618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003025041,0.0001636282,0.01243682,0.0003137478,0.00005561969,0.0001488451,0.0004554292,0.8260641,0.0044,0.004258865,0.01467761,0.1367228],"study_design_scores_gemma":[0.00004462731,0.00009913029,0.007061202,0.00002538228,0.00003213858,0.00001815655,0.0003598608,0.9806506,0.00239098,0.002126965,0.007168778,0.00002213231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6890147,0.001149604,0.2574488,0.001941962,0.000156237,0.0006708932,0.003342281,0.002490788,0.04378479],"genre_scores_gemma":[0.8580409,0.0004461225,0.1335144,0.00008093252,0.00001799131,0.0001598967,0.001492296,0.0001466807,0.006100733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6556367,"threshold_uncertainty_score":0.6847177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05387119972986606,"score_gpt":0.3475262837933807,"score_spread":0.2936550840635146,"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."}}