{"id":"W2604524833","doi":"10.3233/978-1-61499-742-9-49","title":"Diabetes mHealth Apps: Designing for Greater Uptake","year":2017,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Waterloo; University of Toronto; SNC-Lavalin (Canada)","funders":"","keywords":"mHealth; Mobile apps; Internet privacy; App store; Health care; World Wide Web; Computer science; Medical education; Medicine; Nursing; Psychological intervention","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06211362,0.001110922,0.0008999822,0.002854161,0.002665377,0.006730378,0.001854363,0.002849098,0.005306519],"category_scores_gemma":[0.09043704,0.0007296859,0.001707461,0.001452336,0.001429307,0.0098309,0.005805556,0.002270096,0.002409421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002714174,"about_ca_system_score_gemma":0.01035678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004565687,"about_ca_topic_score_gemma":0.005870526,"domain_scores_codex":[0.9633053,0.02070853,0.003342586,0.001960972,0.007752312,0.002930354],"domain_scores_gemma":[0.9520532,0.02296813,0.002002768,0.002743605,0.01771664,0.00251548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004765904,0.001757548,0.04341235,0.005840197,0.0001516708,0.0004112938,0.02089929,0.0004978,0.008809292,0.01259242,0.02119631,0.8839552],"study_design_scores_gemma":[0.003281683,0.01151019,0.1958103,0.01916707,0.003035853,0.003106717,0.06520246,0.01999718,0.04726013,0.05681304,0.5739025,0.0009127444],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.462917,0.01065985,0.2956555,0.05973921,0.002003698,0.05054997,0.001526824,0.008358318,0.1085895],"genre_scores_gemma":[0.4435302,0.002572363,0.5195214,0.006325527,0.0004270848,0.01922609,0.0007209796,0.0007482459,0.006928024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06211362,"threshold_uncertainty_score":0.3284921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.152839650267224,"score_gpt":0.4879089598879338,"score_spread":0.3350693096207098,"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."}}