{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03809173,0.0002244288,0.0003830171,0.001650331,0.001409894,0.002470325,0.0006996358,0.0009878894,0.002178359],"category_scores_gemma":[0.07263555,0.0005113895,0.0007469957,0.002095061,0.0007165843,0.002925033,0.001744313,0.0009768165,0.0006298946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001523636,"about_ca_system_score_gemma":0.003211239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002377051,"about_ca_topic_score_gemma":0.00335306,"domain_scores_codex":[0.9727347,0.01817042,0.002856119,0.0009170379,0.00345184,0.001869948],"domain_scores_gemma":[0.9352598,0.03978652,0.007956143,0.002130528,0.01147538,0.003391617],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002786363,0.001049151,0.8686925,0.0007680155,0.00009440092,0.0006464364,0.07448021,0.0001662105,0.0008842461,0.0005132012,0.002432947,0.04999406],"study_design_scores_gemma":[0.00009658535,0.003053732,0.6419693,0.001279062,0.0001313602,0.003526693,0.3255598,0.00355274,0.001420994,0.0005616305,0.01867995,0.0001680979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948538,0.0001574649,0.001505084,0.001055084,0.0000106356,0.0003768703,0.0002440056,0.00001801745,0.00177906],"genre_scores_gemma":[0.9951228,0.0003463509,0.002517205,0.0009333334,0.00001405351,0.0006808021,0.000153051,0.00001030114,0.0002221115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9619083,"threshold_uncertainty_score":0.2014508,"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."}}