{"id":"W4318141856","doi":"10.1080/10400435.2022.2161669","title":"Clinical stakeholders’ perspective for the integration of an immersive wheelchair simulator as a clinical tool for powered wheelchair training","year":2023,"lang":"en","type":"article","venue":"Assistive Technology","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; GF Strong Rehabilitation Centre; University of British Columbia; University of Manitoba; Centre Integre de Sante et de Services Sociaux de Laval; Université Laval; Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Vancouver Coastal Health; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Wheelchair; Rehabilitation; Context (archaeology); Perspective (graphical); Simulation; Control (management); Focus group; Human–computer interaction; Physical medicine and rehabilitation; Computer science; Applied psychology; Psychology; Physical therapy; Medicine; Artificial intelligence","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":[0.01802824,0.0004469169,0.0003953706,0.0007787559,0.003698884,0.005300286,0.001351482,0.003451932,0.005446389],"category_scores_gemma":[0.03343968,0.0004446764,0.000588374,0.0004511949,0.004070851,0.00334641,0.005182795,0.003434594,0.0007260104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002632883,"about_ca_system_score_gemma":0.007142025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00229307,"about_ca_topic_score_gemma":0.003037473,"domain_scores_codex":[0.9682713,0.02455661,0.0008338454,0.0006809771,0.003011999,0.002645249],"domain_scores_gemma":[0.9718132,0.01372561,0.002627739,0.0005700941,0.00578968,0.005473721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002963699,0.0008438915,0.0443539,0.001041695,0.00007801304,0.01110086,0.8441823,0.0006526706,0.01830555,0.01015137,0.005123276,0.06387012],"study_design_scores_gemma":[0.00003361194,0.001158728,0.01071271,0.0007698126,0.00005995984,0.006758464,0.9194137,0.001434787,0.003420893,0.00311348,0.05300141,0.0001224824],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9081537,0.00179558,0.01865429,0.04908982,0.0002994717,0.0002979635,0.0000526511,0.00009102449,0.02156554],"genre_scores_gemma":[0.9928847,0.0005845418,0.002610308,0.002714692,0.00003411573,0.00009380811,0.00001676365,0.00001630386,0.001044818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01802824,"threshold_uncertainty_score":0.09534359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3243504475656609,"score_gpt":0.5244185978936428,"score_spread":0.2000681503279818,"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."}}