{"id":"W4387798790","doi":"10.1177/08404704231207509","title":"The role of innovative technologies in reducing health system inequity","year":2023,"lang":"en","type":"article","venue":"Healthcare Management Forum","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sciencetech (Canada); Medtronic (Canada)","funders":"","keywords":"Equity (law); Health care; Health equity; Business; Healthcare system; Scarcity; Coronavirus disease 2019 (COVID-19); Healthcare delivery; Pandemic; Knowledge management; Economic growth; Computer science; Political science; Medicine; Economics; Disease; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01150734,0.0004635973,0.0002631768,0.002563098,0.00319742,0.005360504,0.001136571,0.001962963,0.00971025],"category_scores_gemma":[0.0169071,0.0001563602,0.0004983167,0.001186155,0.006105063,0.003950909,0.006899357,0.001952549,0.0005439936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003792012,"about_ca_system_score_gemma":0.01257646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004489205,"about_ca_topic_score_gemma":0.007586006,"domain_scores_codex":[0.9926823,0.004509249,0.000153295,0.0002484522,0.001358898,0.001047932],"domain_scores_gemma":[0.9866023,0.009393977,0.0009523592,0.0006748082,0.0009211112,0.001455464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007395348,0.0007995969,0.01636393,0.001702069,0.000110863,0.0003592792,0.009896797,0.002239851,0.0007967812,0.2771765,0.02468078,0.6657996],"study_design_scores_gemma":[0.0001692931,0.0008297057,0.03168065,0.007720653,0.000176877,0.0006624751,0.02609138,0.004977816,0.002631244,0.5322359,0.3927323,0.00009173347],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1558134,0.04582962,0.02867365,0.3374365,0.001890522,0.0004602408,0.0002180135,0.0003202191,0.4293578],"genre_scores_gemma":[0.9461008,0.01850297,0.01672776,0.01096586,0.0008609967,0.0002644928,0.00004796677,0.00004176456,0.006487326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01150734,"threshold_uncertainty_score":0.0608573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04529214219751165,"score_gpt":0.409954986395422,"score_spread":0.3646628441979104,"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."}}