{"id":"W1986626599","doi":"10.2165/1312067-200801040-00013","title":"Using Conjoint Analysis to Model the Preferences of Different Patient Segments for Attributes of Patient-Centered Care","year":2008,"lang":"en","type":"article","venue":"Patient","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; McMaster University; Hamilton Health Sciences","funders":"Health Canada","keywords":"Latent class model; Conjoint analysis; Population; Health care; Class (philosophy); Psychology; Sample (material); Family medicine; Nursing; Medicine; Computer science; Preference; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01070758,0.001158705,0.001553403,0.001955029,0.0008666802,0.003611426,0.001608969,0.001767398,0.005063694],"category_scores_gemma":[0.02334044,0.0006869239,0.001851235,0.002971601,0.00117652,0.001641081,0.001553506,0.002654055,0.0003817977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00263333,"about_ca_system_score_gemma":0.001473855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01556491,"about_ca_topic_score_gemma":0.01083959,"domain_scores_codex":[0.9903747,0.0083156,0.0001392613,0.0002910107,0.0004628511,0.0004164701],"domain_scores_gemma":[0.9818386,0.01627576,0.0005530766,0.0004284055,0.000486335,0.0004178681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001161775,0.000820562,0.02854694,0.000123959,0.0009530753,0.0003868531,0.000674076,0.8843148,0.0006001405,0.04552501,0.001193,0.03569979],"study_design_scores_gemma":[0.00003656583,0.0001120646,0.001536529,0.00001053049,0.0000659246,0.00006907271,0.0001673101,0.9795685,0.0001010974,0.01809407,0.0002168896,0.00002144181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4168676,0.001146798,0.5737236,0.001895575,0.0001061829,0.0004578583,0.0005941393,0.0001235722,0.005084743],"genre_scores_gemma":[0.9506998,0.000261088,0.04666495,0.0001242791,0.00002690596,0.0002495873,0.0002127804,0.00001442733,0.001746252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01556491,"threshold_uncertainty_score":0.05662781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2224039249311312,"score_gpt":0.2434720714057883,"score_spread":0.02106814647465716,"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."}}