{"id":"W1812979848","doi":"","title":"Number of health care workers lags behind population growth.","year":2000,"lang":"en","type":"article","venue":"PubMed","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Per capita; Health care; Health worker; Population; Geography; Business; Environmental health; Medicine; Health services; Socioeconomics; Economic growth; Economics","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":[0.001267996,0.0003674249,0.0004035475,0.001734292,0.000768962,0.001659063,0.0006402694,0.0007281646,0.007874605],"category_scores_gemma":[0.004357474,0.0001664043,0.0004254887,0.003901303,0.0006173698,0.001117212,0.0005471652,0.001714681,0.001995836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007004386,"about_ca_system_score_gemma":0.01299846,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.794595,"about_ca_topic_score_gemma":0.8674156,"domain_scores_codex":[0.9986823,0.00004598818,0.00005022398,0.0001577911,0.0006304778,0.0004331698],"domain_scores_gemma":[0.9953761,0.0008609887,0.0007139992,0.0001564118,0.001885854,0.001006687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004559987,0.0001297691,0.5349278,0.0006583135,0.000147135,0.0003322188,0.000988355,0.004043773,0.001459254,0.03119534,0.1925321,0.2331299],"study_design_scores_gemma":[0.00001577755,0.00007823035,0.8623669,0.0001932335,0.00005903019,0.0001521878,0.0009905548,0.002784597,0.001198721,0.001823564,0.1303144,0.00002275029],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4125948,0.04101684,0.007674038,0.09141998,0.003705748,0.0001361462,0.1440532,0.00136862,0.2980307],"genre_scores_gemma":[0.9284644,0.01206884,0.001060497,0.002666077,0.000502325,0.00001949499,0.02078096,0.00004953509,0.03438789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.794595,"threshold_uncertainty_score":0.4132293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0431670610476468,"score_gpt":0.2595869466697316,"score_spread":0.2164198856220849,"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."}}