{"id":"W2273564813","doi":"","title":"Reverse engineering of content to find usability problems: a healthcare case study","year":2012,"lang":"en","type":"article","venue":"Journal of Usability Studies archive","topic":"Usability and User Interface Design","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"","keywords":"Usability; Usability engineering; Artifact (error); Computer science; System usability scale; Pluralistic walkthrough; Usability inspection; Reverse engineering; Task (project management); Human–computer interaction; Usability lab; Web usability; Heuristic evaluation; Cognitive walkthrough; Usability goals; Software engineering; Engineering; Artificial intelligence; Systems engineering; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004376605,0.0002701232,0.0008878136,0.0001841529,0.0001566095,0.00003424234,0.0006527767,0.00004473065,0.000005612822],"category_scores_gemma":[0.002754171,0.000212104,0.0002841586,0.0004125686,0.000172137,0.0007190321,0.0007100789,0.0004638677,0.000004963148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000322806,"about_ca_system_score_gemma":0.0001074928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004954423,"about_ca_topic_score_gemma":0.0002786869,"domain_scores_codex":[0.9964526,0.0006285895,0.0014735,0.0003641489,0.0005652088,0.0005159362],"domain_scores_gemma":[0.9958705,0.001440362,0.0005239056,0.0008594482,0.0008910844,0.0004147328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004201123,0.00740312,0.4830224,0.001714496,0.001370154,0.000437295,0.4876661,0.004085904,0.003565372,0.001286394,0.001369286,0.007659392],"study_design_scores_gemma":[0.006338587,0.02878622,0.6269508,0.001803156,0.0006084038,0.01029806,0.3035101,0.005165898,0.003879612,0.005660813,0.004747028,0.002251352],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974867,0.0006208519,0.02011628,0.002679693,0.000676311,0.0009888291,0.00001715109,0.00002534604,0.000008481849],"genre_scores_gemma":[0.9707657,0.0000204175,0.0289189,0.0001481678,0.0001082866,0.00002127991,1.016146e-7,0.000009795794,0.000007397671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.184156,"threshold_uncertainty_score":0.8649349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1618908591463823,"score_gpt":0.3430565840768054,"score_spread":0.1811657249304231,"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."}}