{"id":"W4400522354","doi":"10.2196/54859","title":"Integrating Health and Disability Data Into Academic Information Systems: Workflow Optimization Study","year":2024,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Digital Accessibility for Disabilities","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Workflow; Computer science; Data science; Psychology; World Wide Web; Database","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":[0.01803601,0.0008348472,0.0008348615,0.002937738,0.001710966,0.003880801,0.001427487,0.00115732,0.001515519],"category_scores_gemma":[0.03747311,0.0006087408,0.001565465,0.004161141,0.0009667083,0.002750624,0.001814219,0.001544642,0.0003220325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005342808,"about_ca_system_score_gemma":0.009994274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01600356,"about_ca_topic_score_gemma":0.01137955,"domain_scores_codex":[0.9900389,0.005032017,0.001122774,0.001212519,0.001682651,0.0009111288],"domain_scores_gemma":[0.9462617,0.03688727,0.003803978,0.003925092,0.006986777,0.002135208],"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":[0.002124289,0.01883828,0.2630328,0.002859367,0.0008717556,0.001898077,0.01728146,0.2933711,0.009041069,0.03026745,0.006401419,0.3540129],"study_design_scores_gemma":[0.0004555613,0.003103885,0.06798878,0.0003431429,0.000490936,0.0006188453,0.01417899,0.8806037,0.01189741,0.009973703,0.01014934,0.0001956501],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9176277,0.0004407373,0.07522114,0.0008741045,0.00003560698,0.001346894,0.0005526157,0.0004206981,0.003480594],"genre_scores_gemma":[0.8777901,0.0004128494,0.1190686,0.0001017501,0.00003067441,0.0008538618,0.0009311294,0.00005273311,0.0007583783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01803601,"threshold_uncertainty_score":0.09538466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08013598042247123,"score_gpt":0.4180533183665051,"score_spread":0.3379173379440338,"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."}}