{"id":"W2905622848","doi":"10.5020/18061230.2018.8802","title":"Health system reforms in mature welfare states: tales from the north","year":2018,"lang":"en","type":"article","venue":"Revista Brasileira em Promoção da saúde","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; École Nationale d'Administration Publique; Université de Sherbrooke; Université de Montréal","funders":"","keywords":"Transformative learning; Corporate governance; Welfare reform; Democracy; Government (linguistics); Political science; Healthcare system; Scale (ratio); Welfare state; State (computer science); Welfare; Health policy; Public administration; Economic growth; Public relations; Sociology; Health care; Economics; Politics; Management; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001639544,0.0005025071,0.001148572,0.000155644,0.002134397,0.0001078702,0.0009703386,0.0003571636,0.0004653126],"category_scores_gemma":[0.0001993032,0.0003083146,0.0001678687,0.0008090959,0.0001742014,0.00034853,0.0004050537,0.001633647,0.001001711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002330913,"about_ca_system_score_gemma":0.001925792,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005943893,"about_ca_topic_score_gemma":0.02352664,"domain_scores_codex":[0.9939433,0.001431223,0.001742709,0.0008049307,0.0006088603,0.001469015],"domain_scores_gemma":[0.9963164,0.0005848851,0.0008658135,0.001554032,0.0002733481,0.0004054933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002823697,0.0001160591,0.833077,0.005136527,0.00007184787,0.00006469993,0.01732466,0.000001033984,0.000006824107,0.01275619,0.1158813,0.01528148],"study_design_scores_gemma":[0.000855476,0.0001924211,0.6719058,0.001336278,0.00002127408,0.000008515981,0.007069374,0.00004007351,0.000001581487,0.00005822423,0.3181844,0.0003265309],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9448012,0.005132128,0.0001192247,0.03204949,0.001715801,0.004730028,0.001914327,0.000688093,0.008849717],"genre_scores_gemma":[0.9727601,0.0002567733,0.000454637,0.02285727,0.001406036,0.0003682934,0.000964113,0.0001097673,0.0008230388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2023031,"threshold_uncertainty_score":0.9999369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479949263991062,"score_gpt":0.3798528282104644,"score_spread":0.3350533355705538,"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."}}