{"id":"W2541456986","doi":"10.1007/s13721-016-0140-7","title":"Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks","year":2016,"lang":"en","type":"article","venue":"Network Modeling Analysis in Health Informatics and Bioinformatics","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gini coefficient; Inequality; Statistics; Mathematics; Metric (unit); Measure (data warehouse); Economic inequality; Linear regression; Econometrics; Demography; Economics; Sociology; Computer science; Operations management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003519024,0.0003154095,0.000784484,0.00052284,0.0004754744,0.0002155572,0.0002392023,0.000142367,0.00001190143],"category_scores_gemma":[0.00008265276,0.0001985161,0.0001944944,0.002063724,0.00007003464,0.00046888,0.0001162636,0.0002472458,0.00002990476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000806037,"about_ca_system_score_gemma":0.0001669277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006710386,"about_ca_topic_score_gemma":0.002140583,"domain_scores_codex":[0.9940081,0.0002459832,0.003923138,0.0002071329,0.0004246104,0.001191034],"domain_scores_gemma":[0.9978428,0.0003113104,0.0009500085,0.0004424809,0.00009590242,0.0003574638],"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":[0.00009240946,0.0002280576,0.01595334,0.000144524,0.0001472977,4.367077e-7,0.01682881,0.466252,3.887386e-9,0.001541058,0.0002662964,0.4985458],"study_design_scores_gemma":[0.001091552,0.0005132494,0.003488888,0.0004075009,0.00003832742,0.000001650537,0.002304807,0.9912291,6.667819e-8,0.0002488167,0.0004547614,0.0002213021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2651542,0.0004203445,0.724363,0.007073235,0.000461235,0.001540933,0.0001308125,0.00009644618,0.0007597666],"genre_scores_gemma":[0.9944277,0.00009300953,0.002750712,0.00231942,0.00009743224,0.000165348,0.0000870675,0.00001815991,0.00004116846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7292735,"threshold_uncertainty_score":0.809525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0974556636892333,"score_gpt":0.3926791622232466,"score_spread":0.2952234985340133,"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."}}