{"id":"W3133873938","doi":"10.2196/23914","title":"Prescribing Phones to Address Health Equity Needs in the COVID-19 Era: The PHONE-CONNECT Program","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"McKnight Foundation","keywords":"Phone; Health care; Public health; Health equity; Telemedicine; Digital health; Equity (law); Poverty; Medicine; Intervention (counseling); Internet privacy; Business; Public relations; Nursing; Political science; Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002226942,0.0002203224,0.0002407801,0.00050875,0.001736609,0.001274639,0.0007686031,0.001134936,0.009582723],"category_scores_gemma":[0.0120483,0.0002212018,0.0003994333,0.0004416357,0.0004828753,0.001283424,0.003089074,0.00167886,0.0007412148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001789219,"about_ca_system_score_gemma":0.01019012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02325912,"about_ca_topic_score_gemma":0.09142556,"domain_scores_codex":[0.9985153,0.0007683265,0.00006062795,0.00008544206,0.0002441529,0.0003262149],"domain_scores_gemma":[0.994742,0.001538193,0.0004382536,0.0001881585,0.000329052,0.002764268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001472859,0.01285004,0.07873116,0.000993669,0.00009796365,0.0005459107,0.006625908,0.0002853142,0.001408983,0.00291437,0.08514446,0.8089294],"study_design_scores_gemma":[0.008749344,0.02474597,0.6398259,0.005911453,0.0007578135,0.000981557,0.02719712,0.002748245,0.003637238,0.004237995,0.2810408,0.0001665996],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8414814,0.002832024,0.001844042,0.07751003,0.001039488,0.004168265,0.001269732,0.000403214,0.06945168],"genre_scores_gemma":[0.9267597,0.005911172,0.01220646,0.03248936,0.001166958,0.00498237,0.0008247594,0.00006622348,0.01559286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02325912,"threshold_uncertainty_score":0.04624742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2423234961931464,"score_gpt":0.537369326221916,"score_spread":0.2950458300287696,"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."}}