{"id":"W4396648771","doi":"10.2196/49785","title":"User Preferences and Needs for Health Data Collection Using Research Electronic Data Capture: Survey Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of Mental Health","keywords":"Data collection; Computer science; Electronic data capture; Data science; Automatic identification and data capture; Health records; Survey research; Health care; Psychology; Applied psychology; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.04226136,0.000222664,0.0005723345,0.000509695,0.00140799,0.0001158588,0.001475044,0.0004361584,0.0001157325],"category_scores_gemma":[0.002685111,0.0001715813,0.00001774079,0.001670483,0.0001329342,0.0009294447,0.001446442,0.003127682,0.00005537732],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001649,"about_ca_system_score_gemma":0.02035659,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009627898,"about_ca_topic_score_gemma":0.03175866,"domain_scores_codex":[0.9910616,0.003116254,0.001944516,0.0004243664,0.001618787,0.00183453],"domain_scores_gemma":[0.9926544,0.004452619,0.000285455,0.001648667,0.0003271672,0.0006317037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002744492,0.0003903217,0.0385775,0.01506589,0.0003138894,0.000003411892,0.07831524,0.000001357412,0.000001053418,0.002072519,0.8377406,0.02724377],"study_design_scores_gemma":[0.001742013,0.001490987,0.004379926,0.001833596,0.00003460598,0.0000268565,0.05208314,0.5200714,2.547915e-7,0.0002465944,0.4177752,0.000315411],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9258146,0.007657194,0.0269209,0.006845468,0.004092339,0.02521029,0.001601973,0.000636674,0.001220551],"genre_scores_gemma":[0.9868097,0.001890702,0.001414346,0.001949647,0.001248566,0.001067815,0.003350527,0.0001023975,0.00216627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.52007,"threshold_uncertainty_score":0.9998921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4922842430917665,"score_gpt":0.6140617809307,"score_spread":0.1217775378389334,"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."}}