{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001081206,0.0003632039,0.0006090163,0.0002321989,0.002492949,0.00002986932,0.0004449855,0.0004266751,0.0007809711],"category_scores_gemma":[0.00007004694,0.0003268798,0.0001223857,0.0003132725,0.00005806091,0.0002614344,0.00008105699,0.000811516,0.0001001963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004554546,"about_ca_system_score_gemma":0.0009068978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001233457,"about_ca_topic_score_gemma":0.0003379612,"domain_scores_codex":[0.9957208,0.0005633577,0.001036974,0.0008205411,0.0003583829,0.001499925],"domain_scores_gemma":[0.9967657,0.0007154972,0.0006891266,0.0006026517,0.0002757134,0.0009513391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000844018,0.0009602135,0.9221305,0.004295176,0.00007842415,4.417505e-7,0.01309594,0.0002915494,0.002847682,0.01708574,0.02914882,0.009221528],"study_design_scores_gemma":[0.008511015,0.001946298,0.7238273,0.0005453249,0.00008493791,0.000001421334,0.01161614,0.006380728,0.0003168098,0.0005198203,0.2451362,0.001113979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9579918,0.00008849301,0.01498729,0.0002053134,0.0005196487,0.02201019,0.000248164,0.0005077559,0.003441351],"genre_scores_gemma":[0.9484206,0.00003041322,0.007405071,0.001077265,0.0006324523,0.03823893,0.0007522842,0.0001273362,0.003315674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2159874,"threshold_uncertainty_score":0.9999183,"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."}}