{"id":"W3213316963","doi":"10.2196/24172","title":"Personas for Better Targeted eHealth Technologies: User-Centered Design Approach","year":2021,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Persona Design and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"eHealth; Silhouette; Persona; Computer science; Context (archaeology); Cluster analysis; User-centered design; Set (abstract data type); Process (computing); Human–computer interaction; Iterative and incremental development; Data science; Artificial intelligence; Health care; Geography; Software engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001393231,0.0002077293,0.000219956,0.0001378786,0.000411881,0.0002105904,0.0009609345,0.0001462772,0.00001669494],"category_scores_gemma":[0.00002997323,0.0001887111,0.0001325769,0.00047743,0.00006643011,0.000263789,0.0001604548,0.0001868668,0.00001958494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000323,"about_ca_system_score_gemma":0.0001093573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005874436,"about_ca_topic_score_gemma":0.000002207708,"domain_scores_codex":[0.9983937,0.00006401291,0.0002211271,0.0006415643,0.0002268216,0.0004527534],"domain_scores_gemma":[0.9988493,0.00009715331,0.00009758355,0.0007535056,0.0001073954,0.00009501469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004385591,0.00384488,0.01435257,0.000581608,0.0004072735,0.00004440264,0.02630526,0.0000726574,0.3279565,0.2931661,0.2840499,0.04917504],"study_design_scores_gemma":[0.009486544,0.001642714,0.06926116,0.000222582,0.0001215129,0.00008935854,0.01732468,0.06911482,0.3207746,0.04664034,0.4597363,0.005585388],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07461905,0.0001147855,0.9214806,0.001525928,0.0000817076,0.0009179258,0.00002732859,0.0009170218,0.0003156681],"genre_scores_gemma":[0.8543364,0.000005190018,0.1436359,0.0004373384,0.0000441925,0.0005987142,0.00008670865,0.00002340717,0.0008321453],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7797174,"threshold_uncertainty_score":0.7695414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0888377109385197,"score_gpt":0.313610719949413,"score_spread":0.2247730090108933,"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."}}