{"id":"W4386580558","doi":"10.1016/j.annepidem.2023.08.005","title":"Markers of community outbreak and facility type for mitigation of COVID-19 in long-term care homes in Ontario, Canada: Insights and implications from a time-series analysis","year":2023,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fields Institute for Research in Mathematical Sciences","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Public Health Agency of Canada; National Science Foundation","keywords":"Medicine; Outbreak; Demography; Long-term care; Case fatality rate; Christian ministry; Interrupted Time Series Analysis; Cohort; Coronavirus disease 2019 (COVID-19); Environmental health; Gerontology; Disease; Infectious disease (medical specialty); Statistics; Population","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002312472,0.0004484761,0.0004870874,0.001039778,0.001417264,0.00151808,0.00187029,0.0007844533,0.001239549],"category_scores_gemma":[0.008992021,0.0002931402,0.0008571114,0.002702785,0.0006931733,0.000851465,0.00121254,0.001122257,0.00008578352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01593661,"about_ca_system_score_gemma":0.02994789,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9829449,"about_ca_topic_score_gemma":0.9888641,"domain_scores_codex":[0.9982165,0.0003803574,0.0001452053,0.0002726096,0.0003081048,0.0006773502],"domain_scores_gemma":[0.9937982,0.0009578509,0.00168735,0.0002873295,0.002320429,0.000948784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009114992,0.00003920302,0.9953895,0.00002127376,0.00008722414,0.00003929809,0.000611741,0.0003939394,0.00006548355,0.00009202835,0.0006005834,0.002568668],"study_design_scores_gemma":[0.000003199807,0.00002385903,0.9958203,0.00003140568,0.00004327045,0.00001054531,0.002123467,0.001588377,0.00003021278,0.00004678514,0.000271696,0.00000682923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951493,0.0005974358,0.0006107878,0.0006256955,0.00002138616,0.00003948107,0.002103611,0.00001136563,0.00084103],"genre_scores_gemma":[0.9981557,0.0001947439,0.000311814,0.00006529447,0.00000874775,0.0000148029,0.0007461861,0.00000407684,0.0004985868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01705509,"threshold_uncertainty_score":0.1156287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1484111945665536,"score_gpt":0.442780456548665,"score_spread":0.2943692619821114,"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."}}