{"id":"W2953828951","doi":"10.1590/0102-311x00141218","title":"Health profile differences between recipients and non-recipients of the Brazilian Income Transfer Program in a low-income population","year":2019,"lang":"en","type":"article","venue":"Cadernos de Saúde Pública","topic":"Poverty, Education, and Child Welfare","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Per capita; Environmental health; Transfer payment; Population; Demography; Medicine; Conditional cash transfer; Public health; Per capita income; Gerontology; Poverty; Economics; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001022266,0.0002139575,0.0003182595,0.0009280488,0.0006213292,0.0005532246,0.0002500382,0.0002912681,0.002104973],"category_scores_gemma":[0.004483687,0.0001717488,0.0004760667,0.0008779855,0.0003070948,0.0003490422,0.0006645829,0.0003852146,0.0002393331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005438783,"about_ca_system_score_gemma":0.0004910422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03428354,"about_ca_topic_score_gemma":0.02849495,"domain_scores_codex":[0.9995134,0.0001375487,0.00004631115,0.00007284044,0.00008414112,0.0001457117],"domain_scores_gemma":[0.9991226,0.0001270442,0.0003786267,0.00006134382,0.0001440327,0.0001663963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001180259,0.0001409685,0.9914535,0.00003455511,0.00006155128,0.00008989279,0.001221337,0.00002665937,0.0003516288,0.0001101028,0.0002046508,0.0061871],"study_design_scores_gemma":[0.000006478675,0.0001951252,0.997578,0.00002465413,0.00003437074,0.000130713,0.001286925,0.00008657655,0.00003268447,0.00006401959,0.0005554886,0.000005089628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982548,0.0002014027,0.00005715353,0.0001435592,0.000005217546,0.00002164129,0.0002841889,0.000001669896,0.001030322],"genre_scores_gemma":[0.9992956,0.0001420824,0.0000418757,0.00005428113,0.00000483724,0.00001598216,0.0002122367,0.000001115497,0.0002319871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03428354,"threshold_uncertainty_score":0.06816798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390563648538862,"score_gpt":0.301325258215156,"score_spread":0.2874196217297674,"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."}}