{"id":"W4387736604","doi":"10.1080/07853890.2023.2268109","title":"Heterogenous subtypes of health literacy among individuals with Metabolic syndrome: a latent class analysis","year":2023,"lang":"en","type":"article","venue":"Annals of Medicine","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Health literacy; Latent class model; Social class; Literacy; Medicine; Logistic regression; Multinomial logistic regression; Metabolic syndrome; Univariate analysis; Demography; Gerontology; Psychology; Internal medicine; Multivariate analysis; Obesity; Health care; Sociology; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008179453,0.0001926801,0.001377207,0.001099425,0.0003076915,0.00000466754,0.0002814842,0.0001336886,0.0005954417],"category_scores_gemma":[0.0004442586,0.0001229202,0.0001282187,0.00357922,0.0002581324,0.0005423446,0.00008745654,0.00040529,0.00005461112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000201637,"about_ca_system_score_gemma":0.0003741655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776465,"about_ca_topic_score_gemma":0.0001380304,"domain_scores_codex":[0.9943557,0.001021447,0.002982927,0.0002613294,0.0007320496,0.00064651],"domain_scores_gemma":[0.9954007,0.0006082139,0.002199725,0.0006310453,0.0008556614,0.0003047071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006485816,0.00007734549,0.9635267,0.001468239,0.0005463292,0.000003474873,0.01907774,0.00006694199,0.000003902152,0.0003726997,0.004545548,0.0102462],"study_design_scores_gemma":[0.0009211472,0.0004853157,0.9801424,0.0008335265,0.0001362671,0.000001702256,0.001141092,0.0008303088,0.00003687314,0.0001864403,0.01518525,0.00009963513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846517,0.001520086,0.0003349582,0.01184043,0.0001527421,0.0008017082,0.000130247,0.00009172717,0.0004763361],"genre_scores_gemma":[0.9909501,0.001114889,0.0002318583,0.006887841,0.00005619992,0.00006953371,0.0003283622,0.00001537414,0.0003458238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01793665,"threshold_uncertainty_score":0.6519669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1117473882668034,"score_gpt":0.4729104538493917,"score_spread":0.3611630655825883,"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."}}