{"id":"W2927760739","doi":"10.2196/10271","title":"Appropriation of Mobile Health for Diabetes Self-Management: Lessons From Two Qualitative Studies","year":2019,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ludwig-Maximilians-Universität München; Bundesministerium für Bildung und Forschung; Nanyang Technological University; Universität Erfurt; Diabetes-Stiftung","keywords":"mHealth; CLARITY; Appropriation; Context (archaeology); Perspective (graphical); Value (mathematics); Qualitative research; Digital health; Knowledge management; Computer science; Medicine; Internet privacy; Psychology; Health care; Sociology; Nursing; Geography; Political science","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":[0.04069865,0.0007179035,0.000820604,0.002928296,0.008208225,0.004507856,0.001785818,0.001980397,0.002819412],"category_scores_gemma":[0.0418896,0.0007826829,0.0006287466,0.003364879,0.007496906,0.005936582,0.008258622,0.002431133,0.0003098398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008493403,"about_ca_system_score_gemma":0.005754315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01439166,"about_ca_topic_score_gemma":0.02539845,"domain_scores_codex":[0.9760795,0.01939894,0.001006371,0.0007557025,0.001099771,0.00165962],"domain_scores_gemma":[0.9111909,0.07906486,0.002932028,0.001626184,0.003588819,0.001597122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002942505,0.00004316178,0.002720986,0.0002886882,0.000005844399,0.0004280228,0.9891919,0.00002234068,0.0002946553,0.0009926907,0.0002530122,0.005729231],"study_design_scores_gemma":[0.000007356743,0.00003631477,0.001644311,0.000462314,0.000008412826,0.0001529656,0.9927308,0.00005626447,0.0002821241,0.0003381043,0.004269643,0.00001147881],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778756,0.002521363,0.006107695,0.005139373,0.00009179966,0.0007497621,0.0004363067,0.00002547333,0.007052766],"genre_scores_gemma":[0.9897217,0.002328067,0.003019936,0.001457197,0.00003001043,0.001043128,0.0001210827,0.00003406141,0.00224484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04069865,"threshold_uncertainty_score":0.2152376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07009905217247109,"score_gpt":0.5185015451565923,"score_spread":0.4484024929841212,"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."}}