{"id":"W114133067","doi":"10.3233/tad-2002-14304","title":"The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0): An overview and recent progress","year":2002,"lang":"en","type":"article","venue":"Technology and Disability","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":504,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Equivalence (formal languages); User satisfaction; Consistency (knowledge bases); Relevance (law); Assistive technology; Nomological network; Internal consistency; Measure (data warehouse); Psychology; Test (biology); Factorial analysis; Computer science; Applied psychology; Human–computer interaction; Mathematics; Psychometrics; Artificial intelligence; Clinical psychology; Statistics; Data mining; Political science; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001890137,0.0001705212,0.0002972359,0.0001261168,0.001077151,0.000006273401,0.0002418265,0.0006629481,0.0001570365],"category_scores_gemma":[0.0009133542,0.0001121125,0.00001678687,0.0006800931,0.00579376,0.0001359727,0.0002653261,0.0008115097,0.00000748489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002559995,"about_ca_system_score_gemma":0.00009119191,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003678587,"about_ca_topic_score_gemma":0.03170449,"domain_scores_codex":[0.997816,0.0008065829,0.0004567149,0.0004338337,0.0002021651,0.0002847029],"domain_scores_gemma":[0.997355,0.0004030904,0.0003341436,0.001236986,0.0006208521,0.00004993158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004204832,0.000133601,0.6677014,0.00003341968,0.00002117114,1.191091e-7,0.0000657248,9.697034e-8,0.00001823138,0.03179668,0.00006568451,0.3001218],"study_design_scores_gemma":[0.0007921097,0.0002466934,0.9776403,0.0000670692,0.00009119087,0.000006790693,0.002638388,0.0002598479,0.00007736024,0.01240714,0.005649627,0.0001234428],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682836,0.007111177,0.00004887638,0.02207096,0.00005783011,0.001414121,0.00001178721,0.0003990136,0.0006026649],"genre_scores_gemma":[0.9964643,0.001907562,0.0006392811,0.00005688432,0.000007970204,0.0008348197,0.000005482023,0.00001024622,0.00007344463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.309939,"threshold_uncertainty_score":0.9969119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1306235473973797,"score_gpt":0.4519449367798098,"score_spread":0.32132138938243,"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."}}