{"id":"W2945649616","doi":"10.5539/ibr.v12n6p29","title":"The Use of Information Systems (GIS) to Monitor the Quality of Life of Older People in Greece","year":2019,"lang":"en","type":"article","venue":"International Business Research","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Affect (linguistics); Distribution (mathematics); Quality (philosophy); Quality of life (healthcare); Geographic information system; Information system; Geography; Elderly people; Business; Socioeconomics; Environmental health; Regional science; Economic growth; Gerontology; Psychology; Medicine; Economics; Political science; Cartography; Nursing","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":[],"consensus_categories":[],"category_scores_codex":[0.001647464,0.0001775244,0.0001777429,0.0032567,0.0002163998,0.001188686,0.0002272274,0.0003331142,0.0005574407],"category_scores_gemma":[0.005207251,0.00009207631,0.0002316009,0.002642052,0.000414924,0.001010183,0.0008242913,0.0002355896,0.00007996491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006240806,"about_ca_system_score_gemma":0.0007874352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007153273,"about_ca_topic_score_gemma":0.006230509,"domain_scores_codex":[0.9984717,0.0009154869,0.0001541252,0.0001006518,0.00027421,0.00008384687],"domain_scores_gemma":[0.9980859,0.0008273222,0.0005198373,0.000129825,0.0003200842,0.0001171868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000759879,0.0001580091,0.8287504,0.0004807003,0.0001468425,0.0003305921,0.0102596,0.001469876,0.0008256386,0.001696834,0.001301969,0.1545034],"study_design_scores_gemma":[0.00001122046,0.0004087306,0.9536594,0.0004552611,0.00008983765,0.0005808433,0.03070758,0.002671158,0.0006949995,0.001717056,0.008966517,0.00003736371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892658,0.001553011,0.0009541083,0.0009375656,0.00001978777,0.00005833089,0.0005359831,0.00002144732,0.006653946],"genre_scores_gemma":[0.9972072,0.0009386248,0.001470867,0.00005160108,0.00000913448,0.00001929049,0.0001230779,0.000001779849,0.0001784709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007153273,"threshold_uncertainty_score":0.01422328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1702467902709613,"score_gpt":0.4786291706840616,"score_spread":0.3083823804131004,"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."}}