{"id":"W2744554559","doi":"10.17064/iuifd.333165","title":"NORMAN VE SKINNER’IN E-SAĞLIK OKURYAZARLIĞI ÖLÇEĞİNİN KÜLTÜREL UYARLAMASI İÇİN GEÇERLİLİK VE GÜVENİLİRLİK ÇALIŞMASI","year":2017,"lang":"tr","type":"article","venue":"İstanbul Üniversitesi İletişim Fakültesi Dergisi | Istanbul University Faculty of Communication Journal","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Turkish; Psychology; Literacy; Validity; Equivalence (formal languages); Test (biology); Confirmatory factor analysis; Social psychology; Mathematics education; Psychometrics; Structural equation modeling; Developmental psychology; Pedagogy; Statistics; Mathematics; Linguistics; Philosophy","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":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"category_scores_codex":[0.009107604,0.001751538,0.002881448,0.00235519,0.01501897,0.001130124,0.0101789,0.002226659,0.004117933],"category_scores_gemma":[0.002345869,0.002041305,0.001333126,0.001720977,0.002537624,0.01428443,0.003634568,0.008403984,0.000914907],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006017569,"about_ca_system_score_gemma":0.004030782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002924051,"about_ca_topic_score_gemma":0.002285788,"domain_scores_codex":[0.9817812,0.004894417,0.00591752,0.001608751,0.002755178,0.003042876],"domain_scores_gemma":[0.9720141,0.002181054,0.01203369,0.005949631,0.005648833,0.002172643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01425928,0.007369027,0.3781133,0.006064731,0.002713376,0.001498859,0.2413523,0.002346497,0.0005434182,0.06715273,0.2505237,0.02806271],"study_design_scores_gemma":[0.01712831,0.0008139217,0.2015996,0.005023759,0.0007530905,0.0001844497,0.1250943,0.005147993,0.0001307292,0.002259101,0.6392019,0.002662895],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7781094,0.003317414,0.007766176,0.02752282,0.003189433,0.004955531,0.00406288,0.0004646607,0.1706117],"genre_scores_gemma":[0.9607441,0.007578934,0.01138301,0.001821299,0.0004311762,0.000007194266,0.0009229063,0.0001770547,0.01693434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3886781,"threshold_uncertainty_score":0.9999068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0514006067354849,"score_gpt":0.3752361490109599,"score_spread":0.323835542275475,"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."}}