{"id":"W2811342689","doi":"10.1177/2327857918071009","title":"Medical dispatch decision support for transfer time estimation: Individual operator differences in system use","year":2018,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Numeracy; Variance (accounting); Estimation; Decision support system; Population; Computer science; Psychology; Artificial intelligence; Engineering; Medicine; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006832303,0.0001394031,0.0003388457,0.0002151116,0.000139724,0.00009846487,0.0004382579,0.0001065813,0.00003698757],"category_scores_gemma":[0.0001297407,0.0001173964,0.00006162314,0.00008705811,0.00006791585,0.0001986703,0.0001050309,0.0001368639,0.000004793065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004578519,"about_ca_system_score_gemma":0.00005534761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002064412,"about_ca_topic_score_gemma":0.0008008729,"domain_scores_codex":[0.9984564,0.000005830393,0.0008696233,0.0003268748,0.0001165272,0.0002248053],"domain_scores_gemma":[0.9994226,0.000111567,0.0002036458,0.00008045487,0.00008121755,0.0001005515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002321844,0.0001080208,0.508016,0.000654154,0.00003850823,2.267667e-7,0.007269434,0.0000261098,0.000008567043,0.4821032,0.0005386705,0.001004868],"study_design_scores_gemma":[0.003405415,0.00143239,0.9545625,0.002007866,0.00001063348,0.000004231552,0.002403476,0.01684389,0.0005234503,0.003902127,0.01425499,0.0006490469],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955775,0.00003950321,0.00007493007,0.00241541,0.0005196767,0.0005025684,0.00034099,0.00001104931,0.0005183379],"genre_scores_gemma":[0.9988966,0.00006587715,0.000228898,0.0005455188,0.0001180515,0.00005335729,0.00002410051,0.00001425207,0.00005335995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4782011,"threshold_uncertainty_score":0.4787285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05774051689420964,"score_gpt":0.3070635325692016,"score_spread":0.249323015674992,"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."}}