{"id":"W4396579378","doi":"10.2196/56493","title":"Individual-Level Digital Determinants of Health and Technology Acceptance of Patient Portals: Cross-Sectional Assessment","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Noaber Foundation","keywords":"Cross-sectional study; Environmental health; Computer science; Medicine; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005436684,0.0002045623,0.0002829231,0.001227373,0.0003566761,0.0008676272,0.0003180675,0.000480422,0.001592852],"category_scores_gemma":[0.009129527,0.0003507202,0.0005778323,0.0010426,0.000399518,0.001280445,0.0009803249,0.0007681439,0.0003527869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003005986,"about_ca_system_score_gemma":0.0004205271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001964877,"about_ca_topic_score_gemma":0.002087437,"domain_scores_codex":[0.9980953,0.0008802747,0.0002822605,0.0001908146,0.0003698928,0.0001814574],"domain_scores_gemma":[0.9909726,0.002731001,0.003924082,0.0005813527,0.0009139925,0.0008769868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001643175,0.00007547261,0.9986948,0.000007637671,0.00002177431,0.000009611495,0.0002195927,0.00002027097,0.00003133357,0.000009461472,0.00002757362,0.0008660362],"study_design_scores_gemma":[0.000002933295,0.0002431323,0.9987586,0.000006530061,0.00001547976,0.00008317361,0.00051689,0.0001659618,0.00005581562,0.00001278508,0.0001351204,0.000003687754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990075,0.00004319277,0.0002372675,0.0000317913,0.000001701446,0.00005147184,0.0002758742,0.000003798708,0.0003474008],"genre_scores_gemma":[0.999149,0.00004305227,0.0003641119,0.00003126207,0.00000436647,0.0000712088,0.0002390353,0.000001164263,0.00009676983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005436684,"threshold_uncertainty_score":0.02875233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2084257421268928,"score_gpt":0.5859313658994788,"score_spread":0.377505623772586,"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."}}