{"id":"W2950061073","doi":"10.2196/14250","title":"Assessing Mobile Phone Digital Literacy and Engagement in User-Centered Design in a Diverse, Safety-Net Population: Mixed Methods Study","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Agency for Healthcare Research and Quality","keywords":"Mobile phone; Computer science; Population; Literacy; User-centered design; Phone; Internet privacy; Multimedia; Psychology; Human–computer interaction; Medicine; Environmental health; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"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"],"consensus_categories":[],"category_scores_codex":[0.006839059,0.0004176708,0.00100503,0.0006770123,0.001043342,0.000102762,0.0002059735,0.0003040194,0.0001567752],"category_scores_gemma":[0.0001647073,0.0004037265,0.00004046033,0.00096864,0.00005090284,0.0009320895,0.000274621,0.00154517,0.00007275659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000784458,"about_ca_system_score_gemma":0.001109969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002308078,"about_ca_topic_score_gemma":0.0007443698,"domain_scores_codex":[0.9903824,0.004320011,0.002245697,0.001066115,0.0003764741,0.001609276],"domain_scores_gemma":[0.99533,0.002155946,0.0007372386,0.0006010433,0.000103216,0.00107259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005670208,0.0008689851,0.7255261,0.002567107,0.000009192399,0.000006842486,0.009817785,0.00003595649,0.000005378611,0.0005349788,0.0004239937,0.2596367],"study_design_scores_gemma":[0.00688844,0.0006292983,0.9562315,0.0004462517,0.00002266718,0.00000442194,0.01583383,0.001441947,4.788533e-7,0.0003560699,0.01784275,0.0003023442],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777661,0.001340784,0.001463684,0.001055283,0.0007069614,0.01703157,0.00005369929,0.0001173232,0.0004646271],"genre_scores_gemma":[0.98102,0.001657434,0.007856531,0.001713746,0.0001596781,0.007132192,0.0001507465,0.00005874289,0.0002508765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2593343,"threshold_uncertainty_score":0.9998415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.134116217959388,"score_gpt":0.5287550973420798,"score_spread":0.3946388793826918,"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."}}