{"id":"W3033877789","doi":"10.1080/17483107.2020.1768307","title":"Evaluation of satisfaction with geospatial assistive technology (ESGAT): a methodological and usability study","year":2020,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre for Interdisciplinary Research in Rehabilitation; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université Laval; McGill University; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"","keywords":"Usability; Geospatial analysis; Wheelchair; Pedestrian; Transport engineering; Computer science; Applied psychology; Psychology; Engineering; Human–computer interaction; World Wide Web; Geography; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009401662,0.0003980433,0.0005417229,0.001283874,0.0006181314,0.001064848,0.0003680522,0.0004341216,0.002073829],"category_scores_gemma":[0.01574858,0.0001986435,0.001054531,0.001117526,0.0006813766,0.0005450623,0.00110456,0.0003716596,0.0002684442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016467,"about_ca_system_score_gemma":0.0008343514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531761,"about_ca_topic_score_gemma":0.002252233,"domain_scores_codex":[0.9923077,0.004811993,0.0008183992,0.0002667111,0.001433962,0.0003613789],"domain_scores_gemma":[0.9866261,0.006385905,0.001640872,0.0003810646,0.004348648,0.0006174138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002366052,0.004889688,0.7249435,0.002474939,0.0003347756,0.0008005813,0.07111327,0.0005337753,0.01228919,0.0003900706,0.001434273,0.1784298],"study_design_scores_gemma":[0.0002402455,0.0188335,0.9082509,0.0004346394,0.0003570408,0.001000926,0.05565096,0.001802518,0.007269024,0.0001630414,0.005881489,0.0001157064],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973797,0.00009486316,0.0009454985,0.00003194228,0.000005540001,0.0005435641,0.00008895869,0.00001019715,0.0008998207],"genre_scores_gemma":[0.9953855,0.000141211,0.002173952,0.00005870466,0.000009916516,0.001319128,0.0001604022,0.000008756721,0.0007423693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009401662,"threshold_uncertainty_score":0.04972136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1460145737453523,"score_gpt":0.4675518793540531,"score_spread":0.3215373056087008,"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."}}