{"id":"W2478750292","doi":"10.1016/j.ijmedinf.2016.07.011","title":"Predictors of Internet use for health information among male and female Internet users: Findings from the 2009 Taiwan National Health Interview Survey","year":2016,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"International Business Machines Corporation","keywords":"The Internet; Health Information National Trends Survey; Logistic regression; Respondent; Medicine; Population; Odds ratio; Multivariate analysis; Demography; Family medicine; Odds; National Health Interview Survey; Environmental health; Health information; Gerontology; Health care; Internal medicine; World Wide Web; Computer science; Political science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.020512,0.0002059683,0.0005611217,0.0003208263,0.0001602474,0.0001139239,0.00110724,0.0002414761,0.0007943368],"category_scores_gemma":[0.009865603,0.0001156181,0.0001437327,0.0001677515,0.0002606765,0.005203872,0.0003325076,0.0007237378,0.00003085492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005670182,"about_ca_system_score_gemma":0.002137897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001176239,"about_ca_topic_score_gemma":0.0006253253,"domain_scores_codex":[0.9899436,0.0009756526,0.006400278,0.00009644643,0.002185469,0.0003985653],"domain_scores_gemma":[0.9881335,0.003516573,0.005075059,0.0001928176,0.002592474,0.0004895646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003352572,0.00008733872,0.7078022,0.000557067,0.0002477823,4.127844e-7,0.03382549,0.00000438355,3.919927e-7,0.0009329937,0.2034509,0.0527558],"study_design_scores_gemma":[0.005232436,0.0005620681,0.7633162,0.007360973,0.00001357727,0.00003496955,0.005398673,0.008555992,0.0000146434,0.0003393339,0.208952,0.0002191484],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7915433,0.0001630589,0.1807483,0.02107872,0.003071113,0.0009499247,0.002289998,0.00002527502,0.0001303046],"genre_scores_gemma":[0.9643792,0.001053591,0.003546667,0.02988215,0.0004563342,0.00003618864,0.0004505106,0.0000148889,0.0001804916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1772016,"threshold_uncertainty_score":0.9984747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.104253368719899,"score_gpt":0.446776353944194,"score_spread":0.342522985224295,"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."}}