{"id":"W4402405772","doi":"10.23889/ijpds.v9i5.2746","title":"Understanding Patterns of Care for Older Adults: Data to Action","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Health Quality Council","funders":"","keywords":"Action (physics); Computer science; Data science; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.02294052,0.0008007907,0.0007927423,0.00567289,0.001475783,0.00420129,0.002562629,0.001458597,0.003291183],"category_scores_gemma":[0.069695,0.000471299,0.001161274,0.009707739,0.0007212727,0.004032846,0.004681027,0.002308969,0.000850052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006568977,"about_ca_system_score_gemma":0.02219012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2262982,"about_ca_topic_score_gemma":0.3058164,"domain_scores_codex":[0.9884678,0.005205593,0.002596577,0.00120021,0.001918934,0.000610914],"domain_scores_gemma":[0.95972,0.01346137,0.009104425,0.005261108,0.01024389,0.002209162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001793072,0.0004787235,0.6569659,0.00594167,0.000352706,0.0002885609,0.01081931,0.001296772,0.0008218458,0.006629967,0.09302961,0.2231957],"study_design_scores_gemma":[0.0001302991,0.0002645364,0.7036208,0.0149599,0.0003646394,0.0002456573,0.0352209,0.005411021,0.001137698,0.01556135,0.2229228,0.0001603924],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3110574,0.02690299,0.04245425,0.205604,0.001546367,0.007371475,0.3633472,0.002565664,0.03915059],"genre_scores_gemma":[0.5303316,0.01968249,0.2685832,0.01578247,0.0005476499,0.007277399,0.1535159,0.0002500908,0.004029283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2262982,"threshold_uncertainty_score":0.449962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6292349832746003,"score_gpt":0.5604133632468663,"score_spread":0.06882162002773395,"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."}}