{"id":"W4247920218","doi":"10.2196/preprints.24569","title":"Mapping Research Trends of Universal Health Coverage From 1990 to 2019: Bibliometric Analysis (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scopus; Library science; Bibliometrics; Political science; Medicine; MEDLINE; Computer science; Law","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007646503,0.0005675115,0.001546762,0.1510699,0.0009205192,0.005900078,0.0009585996,0.0008526963,0.007725033],"category_scores_gemma":[0.0542366,0.0003182886,0.002554204,0.2469425,0.0008298884,0.004547963,0.002963859,0.0005987932,0.002014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003774852,"about_ca_system_score_gemma":0.006684295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01809341,"about_ca_topic_score_gemma":0.01323954,"domain_scores_codex":[0.9872423,0.002088594,0.004335818,0.001366016,0.004038803,0.0009284799],"domain_scores_gemma":[0.9396782,0.03251129,0.01362488,0.001446195,0.01154198,0.001197346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005519442,0.0001501067,0.6177258,0.06113732,0.004152089,0.0008743952,0.008903601,0.003036627,0.00185833,0.01000243,0.08531417,0.2062932],"study_design_scores_gemma":[0.00004235463,0.0001250628,0.9004998,0.006915847,0.00140086,0.0007094207,0.009932419,0.002036288,0.0009223086,0.001868192,0.07544535,0.0001021868],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5056961,0.06459065,0.002020919,0.006931605,0.0004869814,0.0007679787,0.3942849,0.0006193185,0.02460162],"genre_scores_gemma":[0.8050235,0.04299083,0.006845665,0.0005608526,0.0006082088,0.001297925,0.1384767,0.0001900174,0.00400616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8489301,"threshold_uncertainty_score":0.04043907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1520016603517241,"score_gpt":0.3571519238028654,"score_spread":0.2051502634511412,"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."}}