{"id":"W4322758297","doi":"10.2196/39055","title":"Critical Criteria and Countermeasures for Mobile Health Developers to Ensure Mobile Health Privacy and Security: Mixed Methods Study","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shiraz University of Technology; Shiraz University; Shiraz University of Medical Sciences","keywords":"mHealth; Confidentiality; Computer science; Content validity; Internet privacy; Reliability (semiconductor); Computer security; Psychology; Medicine; Psychometrics; Nursing; Clinical psychology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3162745,0.001801302,0.005420135,0.01523675,0.004410693,0.009069099,0.004624221,0.003466257,0.004434416],"category_scores_gemma":[0.4291184,0.001916923,0.008783276,0.01122739,0.002590609,0.006860697,0.005751626,0.002603277,0.0006249517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01536358,"about_ca_system_score_gemma":0.03529673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003347298,"about_ca_topic_score_gemma":0.00593439,"domain_scores_codex":[0.7544679,0.1494196,0.06080992,0.005631126,0.02646492,0.003206473],"domain_scores_gemma":[0.4918758,0.3742712,0.04729928,0.01262408,0.07076757,0.003162149],"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":[0.003243939,0.00197651,0.03868807,0.287295,0.005452866,0.001268662,0.1454901,0.0005555064,0.001756971,0.009462593,0.005195776,0.4996141],"study_design_scores_gemma":[0.005153126,0.01543564,0.08851469,0.491211,0.03323511,0.001773174,0.2282376,0.004965317,0.01086018,0.01850695,0.1011583,0.000948835],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4087026,0.1586059,0.07345471,0.01375708,0.001662191,0.3256073,0.002844085,0.0003405194,0.01502567],"genre_scores_gemma":[0.4850532,0.02914041,0.1913965,0.004425743,0.0003844371,0.2865593,0.001085089,0.0001273494,0.001827925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3162745,"threshold_uncertainty_score":0.8431553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1327398177180098,"score_gpt":0.5790040391153475,"score_spread":0.4462642213973377,"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."}}