{"id":"W4319082659","doi":"10.3390/vaccines11020341","title":"Mapping of Pro-Equity Interventions Proposed by Immunisation Programs in Gavi Health Systems Strengthening Grants","year":2023,"lang":"en","type":"article","venue":"Vaccines","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"UNICEF; GAVI Alliance","keywords":"Equity (law); Psychological intervention; Economic growth; Political science; Business; Medicine; Economics; Nursing","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":[0.07578322,0.00106164,0.00101618,0.01557491,0.002210856,0.005785993,0.002581989,0.002094067,0.007227373],"category_scores_gemma":[0.1207557,0.001038054,0.001743427,0.01919288,0.002819942,0.004591324,0.009235621,0.002080627,0.0007740533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01470488,"about_ca_system_score_gemma":0.03253764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005586523,"about_ca_topic_score_gemma":0.005267257,"domain_scores_codex":[0.9151732,0.0628816,0.007388465,0.002687252,0.007471662,0.004397712],"domain_scores_gemma":[0.9115609,0.06494127,0.008072901,0.004094312,0.009592709,0.001737792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001763916,0.0007376603,0.05031471,0.0609696,0.0008764553,0.001705674,0.09855765,0.02640845,0.005264975,0.1469223,0.0172588,0.5892198],"study_design_scores_gemma":[0.001507114,0.002783615,0.1878723,0.04618796,0.002282362,0.0008300697,0.1962792,0.02230828,0.01261365,0.08682019,0.4400669,0.0004483663],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5975049,0.01993571,0.08632817,0.0227726,0.0008732369,0.05773005,0.01749646,0.001035024,0.1963239],"genre_scores_gemma":[0.766406,0.009387676,0.1737937,0.001516863,0.00006732425,0.03972876,0.004695944,0.0001384147,0.004265276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07578322,"threshold_uncertainty_score":0.4007848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026101093422692,"score_gpt":0.3732345459655939,"score_spread":0.2706244366233247,"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."}}