{"id":"W2930205843","doi":"10.2196/12700","title":"Creating an mHealth App for Colorectal Cancer Screening: User-Centered Design Approach","year":2019,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute","keywords":"mHealth; Popularity; Usability; Mobile apps; Context (archaeology); User-centered design; Computer science; Internet privacy; World Wide Web; Medicine; Human–computer interaction; Psychology; Nursing; Psychological intervention","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02909573,0.001323552,0.0006065388,0.002362372,0.002594008,0.004722991,0.002441467,0.002075946,0.002794156],"category_scores_gemma":[0.01932441,0.0009648514,0.001341639,0.001000303,0.003351388,0.00329386,0.004510981,0.002188449,0.000689723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001885789,"about_ca_system_score_gemma":0.004884133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008627089,"about_ca_topic_score_gemma":0.001765322,"domain_scores_codex":[0.9733816,0.02107159,0.001110718,0.001280134,0.002541257,0.0006146356],"domain_scores_gemma":[0.9815538,0.01291624,0.0008461057,0.0009147415,0.002986412,0.0007826284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008509473,0.004498199,0.02659166,0.01343927,0.0004277001,0.001811285,0.3440808,0.008296406,0.03958854,0.05127745,0.01200683,0.4971309],"study_design_scores_gemma":[0.002515249,0.01523568,0.02654177,0.01242051,0.001916859,0.006149359,0.1800954,0.05976676,0.05751867,0.07619368,0.5606767,0.0009693746],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1971431,0.003547459,0.7333694,0.005411824,0.000424532,0.0282222,0.0003313184,0.001342807,0.03020737],"genre_scores_gemma":[0.2065413,0.001270732,0.7741616,0.0009682081,0.00005744856,0.01285185,0.0001238915,0.000119155,0.003905888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02909573,"threshold_uncertainty_score":0.1538748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.171328063326724,"score_gpt":0.4749246605881596,"score_spread":0.3035965972614356,"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."}}