{"id":"W6894274856","doi":"10.5683/sp3/vwggun","title":"Search strategy for Non-take-up of the Government of India's social protection schemes under direct benefit transfer: a scoping review","year":2023,"lang":"fr","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Government (linguistics); MEDLINE; CINAHL; Social protection; EconLit","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":[0.01352307,0.002468659,0.009345483,0.04288736,0.001588854,0.004925933,0.003370512,0.003163727,0.05228706],"category_scores_gemma":[0.07703497,0.001444113,0.00873784,0.04428871,0.0009358119,0.002992153,0.004646797,0.002084373,0.007196036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007499109,"about_ca_system_score_gemma":0.02878099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03387941,"about_ca_topic_score_gemma":0.07817879,"domain_scores_codex":[0.9850172,0.003964443,0.007506783,0.001342335,0.001528978,0.0006403521],"domain_scores_gemma":[0.9455193,0.03404679,0.009665338,0.002623656,0.007079079,0.001065978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001021264,0.00004604655,0.001695796,0.8622129,0.005334751,0.000231408,0.0005031658,0.0002965017,0.0005586683,0.001801909,0.1008919,0.02540564],"study_design_scores_gemma":[0.002731919,0.0002527601,0.009586764,0.5999642,0.02117665,0.0003428906,0.0008374444,0.0002966458,0.0005769086,0.001679965,0.3624025,0.0001514149],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002630843,0.1030875,0.001099196,0.002608488,0.0004394039,0.01219288,0.8697134,0.0003210619,0.007907155],"genre_scores_gemma":[0.04111434,0.2556809,0.01293647,0.005566494,0.0004181453,0.08711774,0.5882615,0.0004595133,0.008444915],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05228706,"threshold_uncertainty_score":0.1749177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08440771030673007,"score_gpt":0.3424171618113096,"score_spread":0.2580094515045795,"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."}}