{"id":"W4390083780","doi":"10.1093/geroni/igad104.3530","title":"POST COVID-19 PANDEMIC EMPLOYMENT RECOVERY BY HEALTHCARE SECTOR IN US RACIAL MAJORITY-MINORITY COUNTIES","year":2023,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Pandemic; Health care; Census; Ethnic group; Demographic economics; Business; Coronavirus disease 2019 (COVID-19); Medicine; Socioeconomics; Economic growth; Geography; Political science; Environmental health; Economics; Population","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":[0.0007464223,0.0001233953,0.0001429363,0.0008015396,0.0006447235,0.0005246381,0.0006090222,0.0002394906,0.001575643],"category_scores_gemma":[0.002385851,0.0001178428,0.0003176135,0.001129383,0.0002359697,0.0005299465,0.001256328,0.0004819604,0.0002043108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008168209,"about_ca_system_score_gemma":0.001196537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09904124,"about_ca_topic_score_gemma":0.1580371,"domain_scores_codex":[0.9994546,0.000115056,0.00004774061,0.00007641991,0.00007452822,0.0002315558],"domain_scores_gemma":[0.9984801,0.0001578675,0.0006414716,0.00006390795,0.0003430513,0.000313576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006874563,0.00006252458,0.9945058,0.00001479102,0.00002564724,0.00005209036,0.0004472886,0.0001288405,0.0001611098,0.00007283303,0.001102821,0.003357515],"study_design_scores_gemma":[0.000002118383,0.00002844561,0.9972528,0.00001705545,0.000005306538,0.00001702168,0.001950619,0.0002296608,0.00006362628,0.00001915815,0.0004117824,0.000002375862],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981503,0.00007777139,0.00004085293,0.0002608823,0.000007909443,0.00001005541,0.001041515,0.000002559464,0.0004082621],"genre_scores_gemma":[0.9981628,0.00007516542,0.00006797459,0.0001096672,0.000009026884,0.00002640938,0.001277245,0.000001925813,0.0002698825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09904124,"threshold_uncertainty_score":0.1969295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08865707572389363,"score_gpt":0.4272545422117717,"score_spread":0.3385974664878781,"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."}}