{"id":"W4393016809","doi":"10.2196/49719","title":"Leveraging mHealth Technologies for Public Health","year":2024,"lang":"en","type":"review","venue":"JMIR Public Health and Surveillance","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; University Health Network; University of Toronto; University of Waterloo","funders":"","keywords":"Preprint; Public health; Computer science; Internet privacy; Data science; Medicine; World Wide Web; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.02817256,0.0007910926,0.0009370731,0.008035844,0.002679733,0.01131677,0.002566271,0.00229761,0.005365084],"category_scores_gemma":[0.05017167,0.000488937,0.001581623,0.008349953,0.004780625,0.009406136,0.009105636,0.003344856,0.001031959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01735985,"about_ca_system_score_gemma":0.04673415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4216829,"about_ca_topic_score_gemma":0.4065988,"domain_scores_codex":[0.980334,0.0073884,0.00118525,0.001239013,0.007828576,0.002024778],"domain_scores_gemma":[0.9609805,0.02103904,0.002843195,0.00278691,0.01046348,0.001886896],"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":[0.0001060652,0.0001524347,0.0230448,0.007566635,0.0004015807,0.0003419377,0.01040994,0.0008890884,0.002335711,0.06313147,0.04868288,0.8429375],"study_design_scores_gemma":[0.00006059732,0.0003433609,0.06276304,0.02648872,0.0007831514,0.000385336,0.0142643,0.001565579,0.003304106,0.05234821,0.8374315,0.0002622103],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.03463346,0.2272289,0.0485803,0.4334194,0.004158231,0.002153265,0.008098613,0.001431579,0.2402962],"genre_scores_gemma":[0.4770551,0.2972613,0.150456,0.05539631,0.003826497,0.001335482,0.003644851,0.0002426618,0.01078179],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.4216829,"threshold_uncertainty_score":0.8384568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2334718907257327,"score_gpt":0.5004824323023546,"score_spread":0.2670105415766219,"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."}}