{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01682691,0.0001348243,0.0004685018,0.0003047455,0.0001919648,0.00009331512,0.001014231,0.0001161707,0.001106353],"category_scores_gemma":[0.02103261,0.00006420892,0.000197278,0.001341703,0.0003561303,0.00007073101,0.0007759568,0.002874752,0.00002044708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003200604,"about_ca_system_score_gemma":0.00225016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0011469,"about_ca_topic_score_gemma":0.001708768,"domain_scores_codex":[0.9927616,0.001617459,0.0007989161,0.0002012702,0.003942427,0.0006783119],"domain_scores_gemma":[0.9960679,0.001875847,0.000130615,0.0003484071,0.0007734154,0.0008038476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004872623,0.0007401524,0.002846806,0.000347069,0.0002235327,0.002356547,0.02686438,0.000003496095,0.0002297523,0.0008663909,0.8949887,0.0700459],"study_design_scores_gemma":[0.003256172,0.003486505,0.005281085,0.002338666,0.00005545954,0.003750481,0.06925756,0.0002428639,0.001552062,0.0007086478,0.9098835,0.00018702],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2821459,0.01557657,0.0002294171,0.6972174,0.0008015307,0.0007718441,0.000004999641,0.00001617476,0.003236098],"genre_scores_gemma":[0.9698842,0.005202976,0.0001145484,0.02270422,0.001256527,0.00006324289,0.000004085673,0.0000144849,0.0007556769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6877383,"threshold_uncertainty_score":0.9998068,"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."}}