{"id":"W4416769941","doi":"10.3389/fdgth.2025.1680350","title":"Editorial: Socioeconomic inequalities in digital health","year":2025,"lang":"en","type":"article","venue":"Frontiers in Digital Health","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital health; Digital divide; Inequality; Population; Digital transformation; Socioeconomic status; Preparedness; Health equity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006626955,0.0002209755,0.0008048779,0.0007383536,0.00008399434,0.00007997236,0.0001208733,0.0001111063,0.00001536215],"category_scores_gemma":[0.0002119691,0.0002265601,0.00007210321,0.000407624,0.00009018786,0.0006195322,0.00005468804,0.0004248648,0.0000184603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001888271,"about_ca_system_score_gemma":0.002599208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009518246,"about_ca_topic_score_gemma":0.0002761727,"domain_scores_codex":[0.9972032,0.00003610086,0.001285609,0.0004025943,0.0002744236,0.0007980908],"domain_scores_gemma":[0.9990225,0.0001077143,0.0002527467,0.0002689626,0.00005482002,0.000293212],"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":[0.0001372245,0.000132665,0.3374948,0.0005669623,0.00001336394,0.000002888146,0.0009389652,0.000001597486,7.078361e-8,0.000542787,0.5461326,0.1140361],"study_design_scores_gemma":[0.01232365,0.002043633,0.1702244,0.001229073,0.000008925443,0.00001110512,0.02132826,0.000202064,0.000006091042,0.02440651,0.7677923,0.0004239576],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.725741,0.008533253,0.006826641,0.09731812,0.1396072,0.00553157,0.0008323477,0.000482773,0.01512705],"genre_scores_gemma":[0.9771585,0.0005793296,0.0008911611,0.0118451,0.007183824,0.00008663893,0.0008454807,0.00003967249,0.001370243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2514175,"threshold_uncertainty_score":0.9238853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858658877464718,"score_gpt":0.3448378903045736,"score_spread":0.3262513015299264,"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."}}