{"id":"W3150581439","doi":"","title":"Health Shocks, Human Capital, and Labor Market Outcomes","year":2018,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Global Health Care Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; Institut für Arbeitsmarkt- und Berufsforschung; W.E. Upjohn Institute for Employment Research","keywords":"Human capital; Health care; Labour economics; Economics; Business; Demographic economics; Economic growth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001431286,0.0003293354,0.0005403424,0.0007706144,0.000445929,0.002038023,0.0007468143,0.0007505238,0.005376292],"category_scores_gemma":[0.005508435,0.000325492,0.0005113464,0.001427074,0.001040297,0.0009142887,0.001342798,0.001127603,0.0004451917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001827885,"about_ca_system_score_gemma":0.001847335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06790739,"about_ca_topic_score_gemma":0.05142866,"domain_scores_codex":[0.9993671,0.0002124726,0.00002845084,0.0001106335,0.00005426369,0.0002272017],"domain_scores_gemma":[0.9962425,0.002111552,0.001016602,0.0002242386,0.00008810197,0.0003170015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001630837,0.0003314626,0.8933643,0.00009519547,0.0003776767,0.0004186049,0.0005440728,0.07240555,0.0004482276,0.01656366,0.001789326,0.0134988],"study_design_scores_gemma":[0.0001054136,0.0002690691,0.8413987,0.0001841836,0.0002184983,0.000166654,0.002141753,0.116704,0.0006802867,0.031481,0.00656562,0.00008480997],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853446,0.0004668554,0.005146031,0.002015344,0.00001848255,0.00005855922,0.003010295,0.0000388895,0.003900937],"genre_scores_gemma":[0.996781,0.0002228568,0.0004835658,0.00007419253,0.00001335536,0.00002401637,0.0008908849,0.000002506191,0.001507635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06790739,"threshold_uncertainty_score":0.1350242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06171374520099951,"score_gpt":0.4779743368453184,"score_spread":0.4162605916443189,"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."}}