{"id":"W3012594047","doi":"10.1080/17483107.2020.1741703","title":"Assistive technology use and unmet need in Canada","year":2020,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; GF Strong Rehabilitation Centre","funders":"","keywords":"Context (archaeology); Legislation; Population; Government (linguistics); Pace; Sample (material); Needs assessment; Psychology; Environmental health; Medicine; Gerontology; Geography; Political science","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":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007048222,0.0003521203,0.0007667241,0.0003385402,0.0006136348,0.00001145274,0.0003760547,0.0008327375,0.0001000527],"category_scores_gemma":[0.01192113,0.0003350829,0.00005150785,0.001706386,0.003863188,0.0002261555,0.0007221582,0.001866795,0.00001485806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302117,"about_ca_system_score_gemma":0.0008275367,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09936694,"about_ca_topic_score_gemma":0.5325238,"domain_scores_codex":[0.9962749,0.0008766744,0.001030705,0.001021561,0.0002025902,0.0005935081],"domain_scores_gemma":[0.9938422,0.004340089,0.0003658072,0.0008421484,0.0004075427,0.0002021754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001513507,0.0001277057,0.9761226,0.0001557326,0.00003093122,0.000003331654,0.0002031778,0.00000181689,0.0002176425,0.01276022,0.0004992998,0.009726189],"study_design_scores_gemma":[0.001049202,0.0002665569,0.967463,0.00008590632,0.00002567323,0.000002441933,0.02182177,0.0003180392,0.00003136316,0.001700402,0.006931787,0.0003039002],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8672364,0.0002332095,0.0003606001,0.1298212,0.0001065235,0.001296419,0.0001149341,0.0004340675,0.0003966037],"genre_scores_gemma":[0.9928037,0.00007607921,0.005114407,0.001268256,0.00001665064,0.0006402378,0.00002576768,0.00002500984,0.00002989475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4331568,"threshold_uncertainty_score":0.9999101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04160851924524039,"score_gpt":0.356179637899674,"score_spread":0.3145711186544336,"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."}}