{"id":"W3036656988","doi":"10.3233/shti200317","title":"Online Medication Information for Citizens: A Comparison of Demands on eHealth Literacy","year":2020,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"eHealth; Health literacy; Checklist; Literacy; Variety (cybernetics); The Internet; Medical prescription; Internet privacy; Strengths and weaknesses; Health information; Information overload; Medical education; World Wide Web; Medicine; Computer science; Psychology; Health care; Nursing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004081707,0.000196169,0.0005372807,0.002473118,0.0009364697,0.003247535,0.0005307715,0.0008648088,0.005142918],"category_scores_gemma":[0.04260977,0.0002769584,0.0008910062,0.0022545,0.001071204,0.003913515,0.003647111,0.001069727,0.000449477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001322544,"about_ca_system_score_gemma":0.001433785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004015079,"about_ca_topic_score_gemma":0.005206801,"domain_scores_codex":[0.9936244,0.00217955,0.001353122,0.0002571187,0.001841176,0.0007444891],"domain_scores_gemma":[0.9607441,0.02260916,0.008229399,0.0009448136,0.005040734,0.002431773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00153343,0.001925537,0.8632519,0.0008474996,0.0002062237,0.0005326337,0.05953323,0.0001331147,0.001043947,0.001325816,0.001129573,0.06853704],"study_design_scores_gemma":[0.00005263522,0.0007423708,0.9185221,0.0002623789,0.00009131627,0.0002652144,0.07631463,0.0003164261,0.0002912128,0.0002996405,0.002812806,0.00002928625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942993,0.0001668328,0.00006509458,0.0002289872,0.00001011821,0.00004131745,0.0001276277,0.000004795731,0.005056011],"genre_scores_gemma":[0.9986739,0.0002563387,0.0001862584,0.0001167291,0.00001639698,0.00007901379,0.0002069189,0.000008129779,0.0004561828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005142918,"threshold_uncertainty_score":0.02158642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1507074851619943,"score_gpt":0.5419118422513997,"score_spread":0.3912043570894054,"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."}}