{"id":"W2364004692","doi":"","title":"The Distribution of Nursing Personnel in Terms of Nursing Load","year":2000,"lang":"en","type":"article","venue":"Hospital Administration J Chinese Pla","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nursing; Distribution (mathematics); Medicine; Work (physics); Quality (philosophy); Engineering; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008859851,0.0002204331,0.0002380568,0.002540787,0.0007604107,0.002048279,0.0005620222,0.0002955094,0.005206606],"category_scores_gemma":[0.004126805,0.0001204056,0.0002388314,0.002086429,0.0007315999,0.001325232,0.001199797,0.0004078922,0.001804821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001777649,"about_ca_system_score_gemma":0.001074287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005228328,"about_ca_topic_score_gemma":0.003861102,"domain_scores_codex":[0.998001,0.0004116661,0.0001956105,0.0001874903,0.0009129921,0.0002911802],"domain_scores_gemma":[0.9981642,0.0002661248,0.0002566593,0.0001366653,0.0008395947,0.0003367037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001190019,0.0004102805,0.4322789,0.0003581295,0.0001684311,0.0007013796,0.01057281,0.008359038,0.01866854,0.06740103,0.01398149,0.4459099],"study_design_scores_gemma":[0.00004768176,0.0006960323,0.8764401,0.000142651,0.00006404117,0.0009762836,0.00843731,0.01324884,0.003701514,0.03323209,0.06289907,0.000114525],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8461884,0.002020252,0.02566884,0.00344601,0.00062016,0.0002020475,0.001235471,0.0002656832,0.1203532],"genre_scores_gemma":[0.9848263,0.0005094106,0.002003948,0.0001128116,0.0002563015,0.0000627316,0.0002669634,0.00003865114,0.01192288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005228328,"threshold_uncertainty_score":0.01741785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424978965250757,"score_gpt":0.3140173047693423,"score_spread":0.2997675151168347,"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."}}