{"id":"W2104399119","doi":"10.1177/0897190009358772","title":"The Use of Queueing and Simulative Analyses to Improve an Overwhelmed Pharmacy Call Center","year":2010,"lang":"en","type":"article","venue":"Journal of Pharmacy Practice","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Staffing; Queueing theory; Pharmacy; Medicine; Discrete event simulation; Desk; Service (business); Call management; Medical emergency; Computer science; Operations management; Operations research; Computer network; Call control; Simulation; Engineering; Nursing; Operating system; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001533156,0.000773046,0.0003868887,0.0006850231,0.0004492904,0.0009657421,0.0005744083,0.0006004597,0.0009467884],"category_scores_gemma":[0.007211174,0.0003429935,0.0003790643,0.0004576363,0.0005763807,0.0008026735,0.0003711471,0.0004867544,0.00006430299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002196001,"about_ca_system_score_gemma":0.00224908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02525559,"about_ca_topic_score_gemma":0.01040647,"domain_scores_codex":[0.9991794,0.0004301617,0.00004014099,0.00008508779,0.0001938906,0.0000711976],"domain_scores_gemma":[0.9951806,0.003681739,0.0004383737,0.0001524524,0.000466903,0.00007987124],"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":[0.00005045104,0.00006626802,0.0009994457,0.00002317816,0.00001374416,0.00001849962,0.00004618129,0.9923299,0.0007366292,0.001601922,0.0001094774,0.004004361],"study_design_scores_gemma":[0.000008145893,0.0000371745,0.0002498635,0.000002724903,0.000004940433,0.000002937025,0.00002342119,0.9987686,0.0004173377,0.0003842204,0.00009597297,0.000004605789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.646003,0.0002376478,0.3459117,0.0006721257,0.00007820244,0.0002257792,0.0001542458,0.0007429454,0.005974413],"genre_scores_gemma":[0.9732552,0.00007132828,0.02614288,0.00003217511,0.000009901095,0.00004752485,0.00003973418,0.00001480612,0.0003865759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02525559,"threshold_uncertainty_score":0.05021715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2940790667130538,"score_gpt":0.5890786166267872,"score_spread":0.2949995499137333,"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."}}