{"id":"W2221938488","doi":"10.14236/jhi.v15i3.653","title":"Assessing medical student learning in assessing the quality ofhealth information on the internet and communicating the skill topatients","year":2007,"lang":"en","type":"article","venue":"Journal of Innovation in Health Informatics","topic":"Innovations in Medical Education","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Checklist; The Internet; Medical education; Formative assessment; Likert scale; Medicine; Session (web analytics); Quality (philosophy); Information quality; Psychology; Family medicine; Information system; Mathematics education; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0617454,0.0001226669,0.0002954086,0.0005726772,0.0003641966,0.0001932598,0.0003440821,0.0001153749,0.00001495916],"category_scores_gemma":[0.02285733,0.00006287607,0.00002321895,0.002234821,0.0001714916,0.001040353,0.00009911106,0.002655202,0.000001989261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007348566,"about_ca_system_score_gemma":0.001045009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009185391,"about_ca_topic_score_gemma":0.00002051148,"domain_scores_codex":[0.9903745,0.0005347734,0.00713588,0.00005128425,0.001589158,0.000314386],"domain_scores_gemma":[0.9903513,0.002111241,0.005938774,0.0003294114,0.001201703,0.0000675767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005569627,0.000237324,0.3721472,0.0005557812,0.00002064705,0.000001405291,0.07575537,0.0003485898,0.000001954623,0.02036362,0.0009339128,0.5295785],"study_design_scores_gemma":[0.001293176,0.0002829485,0.8254334,0.00268049,0.000005721784,0.0001208907,0.1368844,0.02347458,0.00001414182,0.000270037,0.009453208,0.00008701337],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9308221,0.00004246773,0.01590659,0.05126755,0.0004098045,0.0004788445,2.07578e-7,0.000008917192,0.001063553],"genre_scores_gemma":[0.9627978,0.00009786031,0.005264754,0.03170034,0.0001032213,0.000008226646,0.00001488943,0.000006595583,0.000006306473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5294915,"threshold_uncertainty_score":0.9996457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0790028365308855,"score_gpt":0.5117263354344438,"score_spread":0.4327234989035583,"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."}}