{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002132987,0.0001413182,0.0003075658,0.0001394823,0.001301382,0.0001550595,0.00106184,0.00005364784,0.0007062604],"category_scores_gemma":[0.0004984774,0.00007335527,0.0003012721,0.0007533355,0.0002173615,0.0001783408,0.0002551492,0.0004469143,0.000004946422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005341136,"about_ca_system_score_gemma":0.0002852524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002305054,"about_ca_topic_score_gemma":0.00005802002,"domain_scores_codex":[0.9964797,0.00007697431,0.0009572504,0.0001664532,0.002064841,0.0002547239],"domain_scores_gemma":[0.9971849,0.0004409617,0.001193092,0.0006219572,0.0004700366,0.00008909954],"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":[0.00007923619,0.00007853279,0.005080554,0.000005278152,0.00005626728,7.097838e-7,0.002323807,0.9785236,0.00007993067,0.002196155,0.000152562,0.01142337],"study_design_scores_gemma":[0.008824462,0.002029847,0.1174676,0.001080068,0.001792828,0.0006340637,0.254793,0.5548309,0.02375463,0.02422759,0.008287432,0.002277572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9432424,0.00146451,0.03907478,0.0007212524,0.001049297,0.0003036526,0.00006329131,0.00002169731,0.01405912],"genre_scores_gemma":[0.9974898,0.00007967621,0.001753086,0.00009520938,0.00008377417,0.000003300844,0.00000599095,0.00001056298,0.0004785766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4236927,"threshold_uncertainty_score":0.9999988,"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."}}