{"id":"W2595658265","doi":"","title":"Health Care in Canada to Overcome Income Inequality","year":2016,"lang":"en","type":"article","venue":"Journal of Legal Ethical and Regulatory Issues","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic inequality; Inequality; Economics; Demographic economics; Income distribution; Income inequality metrics; Development economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002006292,0.0004435505,0.0004465998,0.00340106,0.006344101,0.005091239,0.002115232,0.001564972,0.02246482],"category_scores_gemma":[0.00889785,0.000185135,0.0007300691,0.005951123,0.001844132,0.001296139,0.003316366,0.002617253,0.001171728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09712975,"about_ca_system_score_gemma":0.2539388,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9910753,"about_ca_topic_score_gemma":0.9951189,"domain_scores_codex":[0.9944502,0.0004187011,0.0001182483,0.0002195077,0.002375076,0.002418366],"domain_scores_gemma":[0.9927885,0.0004890584,0.0003513382,0.0001657304,0.003987086,0.002218307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006020337,0.0001207888,0.04244437,0.0009179573,0.00006584793,0.0004566557,0.002623936,0.0007884435,0.0003090989,0.138835,0.5623525,0.2510252],"study_design_scores_gemma":[0.00004507808,0.00004847998,0.09419682,0.002120962,0.0000671468,0.0002127554,0.004482669,0.0009221082,0.000408997,0.006499845,0.8909431,0.00005199793],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.03808778,0.05627502,0.002670848,0.3216643,0.004685229,0.0004162968,0.01101552,0.0004781116,0.5647069],"genre_scores_gemma":[0.6613421,0.06511658,0.01105585,0.08870108,0.00243477,0.0003997858,0.007653631,0.0002421865,0.1630539],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09712975,"threshold_uncertainty_score":0.704729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563873283441302,"score_gpt":0.3345508405722317,"score_spread":0.3189121077378186,"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."}}