{"id":"W4210611528","doi":"10.2196/preprints.10318","title":"Using Human-Centered Design to Build a Digital Health Advisor for Patients With Complex Needs: Persona and Prototype Development (Preprint)","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Persona Design and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women's College Hospital; University of Toronto","funders":"","keywords":"Digital health; Preprint; Persona; General partnership; Health care; Key (lock); Public relations; Knowledge management; Computer science; Psychology; Medical education; Medicine; World Wide Web; Business; Political science; Human–computer interaction; Computer security","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":[0.01329384,0.0007551602,0.0004143371,0.0007689862,0.001242399,0.003686534,0.002233034,0.001758299,0.01108736],"category_scores_gemma":[0.02116733,0.0005498383,0.0008129888,0.0004069159,0.001690748,0.002694629,0.003525307,0.001238652,0.001848459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288561,"about_ca_system_score_gemma":0.002800721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001394309,"about_ca_topic_score_gemma":0.001671158,"domain_scores_codex":[0.9929907,0.005160349,0.0003096011,0.0004041308,0.000836965,0.0002982071],"domain_scores_gemma":[0.9888735,0.007405133,0.0002861877,0.001142304,0.001626915,0.0006659457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001177951,0.009617306,0.01456309,0.005834988,0.00028922,0.003641582,0.175385,0.01248199,0.04830186,0.03755252,0.05118027,0.6399742],"study_design_scores_gemma":[0.00306368,0.01943098,0.02159363,0.005919853,0.0006396176,0.003886874,0.09488567,0.04733913,0.06367211,0.02575675,0.7130788,0.0007329333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3880423,0.000877846,0.530822,0.004698998,0.0008434028,0.0167019,0.001091857,0.004691015,0.05223066],"genre_scores_gemma":[0.2854764,0.0005637993,0.6864606,0.001003744,0.00005035201,0.01022044,0.0005980398,0.0004552946,0.01517127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01329384,"threshold_uncertainty_score":0.07030541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1363060160184488,"score_gpt":0.3345526514259597,"score_spread":0.198246635407511,"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."}}