{"id":"W3008964144","doi":"10.2196/16473","title":"Digital Health and Inequalities in Access to Health Services in Bangladesh: Mixed Methods Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; International Centre for Diarrhoeal Disease Research, Bangladesh; International Development Research Centre","keywords":"Health care; Digital divide; Business; mHealth; Focus group; Environmental health; Socioeconomic status; Economic growth; Medicine; Internet privacy; Information and Communications Technology; Population; Marketing; Computer science; World Wide Web; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005674182,0.0006092203,0.001888316,0.0008580587,0.001348611,0.0001156764,0.0005484237,0.0003016129,0.00004516213],"category_scores_gemma":[0.0002769366,0.0005954196,0.00004955538,0.002205649,0.0000905793,0.0006718891,0.0006463664,0.001871136,0.0000547975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008122486,"about_ca_system_score_gemma":0.005268702,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008786857,"about_ca_topic_score_gemma":0.01553646,"domain_scores_codex":[0.9874957,0.003825147,0.003628644,0.001605001,0.0005276473,0.002917891],"domain_scores_gemma":[0.9904786,0.001292356,0.001082324,0.0006409351,0.0001340134,0.006371789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007574735,0.000640979,0.5078309,0.02458525,0.000008979454,0.000006775594,0.09436555,0.000008754866,0.000001034898,0.002061218,0.005240439,0.3644926],"study_design_scores_gemma":[0.005567831,0.003033461,0.7836545,0.001081655,0.000008334892,0.000005534908,0.09922331,0.0005499278,3.677888e-7,0.0006376227,0.1057119,0.0005256117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8271923,0.006759514,0.0004615048,0.1439536,0.0004709471,0.01989307,0.0002695474,0.000312322,0.0006871932],"genre_scores_gemma":[0.8408589,0.004269987,0.002198276,0.1431517,0.0003978951,0.008782992,0.0001566877,0.00009904751,0.00008450634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.363967,"threshold_uncertainty_score":0.9999515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1652305603390703,"score_gpt":0.542669049524732,"score_spread":0.3774384891856618,"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."}}