{"id":"W4225390865","doi":"10.1177/23337214221090803","title":"Who’s in the House? Staffing in Long-Term Care Homes Before and During COVID-19 Pandemic","year":2022,"lang":"en","type":"article","venue":"Gerontology and Geriatric Medicine","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; McMaster University; University Health Network; Health Sciences Centre","funders":"National Institute for Health and Care Research","keywords":"Staffing; Long-term care; Workforce; Pandemic; Nursing; Workforce planning; Recreation; Medicine; Work (physics); Coronavirus disease 2019 (COVID-19); Psychology; Political science; Disease","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":[0.003437792,0.0001002587,0.0002087551,0.0004366473,0.001360076,0.0009348766,0.0004805683,0.0004781081,0.001228675],"category_scores_gemma":[0.009647985,0.000178342,0.0002578827,0.0005363324,0.001117538,0.001366952,0.001254255,0.0007846042,0.0001614294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002943228,"about_ca_system_score_gemma":0.003445189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04893978,"about_ca_topic_score_gemma":0.07839783,"domain_scores_codex":[0.9976162,0.0008983297,0.000188264,0.0001632628,0.0003652151,0.0007687956],"domain_scores_gemma":[0.9952709,0.0009326224,0.001465233,0.0001772674,0.0009276603,0.001226296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003255055,0.0001410716,0.8591635,0.000212108,0.00005418349,0.0004812112,0.07419426,0.0005648215,0.000633568,0.0007886495,0.007006077,0.05643507],"study_design_scores_gemma":[0.00000620935,0.0002153974,0.8659298,0.0004263463,0.00001469985,0.0002320696,0.1273016,0.0002923572,0.0002605434,0.0003134229,0.004971435,0.00003602956],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969612,0.0002820514,0.0001180835,0.001362685,0.00003487192,0.00000829116,0.0001066375,0.000003000455,0.001123195],"genre_scores_gemma":[0.9992272,0.000188613,0.0001346426,0.0002210797,0.00001232093,0.00001017484,0.00006073683,0.000001894542,0.0001434545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04893978,"threshold_uncertainty_score":0.09730983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04586803446866048,"score_gpt":0.3892443766168104,"score_spread":0.3433763421481499,"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."}}