{"id":"W4400138724","doi":"10.1016/j.ssmhs.2024.100016","title":"Mind the data gaps: Comparing the quality of data sources for maternal health services in Cameroon","year":2024,"lang":"en","type":"article","venue":"SSM - Health Systems","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Affairs Canada; University of Ottawa","funders":"","keywords":"Quality (philosophy); Data quality; Business; Environmental health; Data science; Geography; Computer science; Medicine; Marketing; Physics","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":[0.105012,0.0006013425,0.001030177,0.01536247,0.001526274,0.005186491,0.002438739,0.001046386,0.001375945],"category_scores_gemma":[0.3844631,0.0009205087,0.0009484365,0.03115552,0.002051186,0.006107236,0.006025463,0.001038014,0.0001676503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006611866,"about_ca_system_score_gemma":0.009265982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08847298,"about_ca_topic_score_gemma":0.07104237,"domain_scores_codex":[0.8544191,0.09711863,0.01772529,0.004887497,0.02188766,0.003961905],"domain_scores_gemma":[0.6140838,0.2735018,0.05224017,0.01605645,0.04148102,0.002636706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005671589,0.00006353057,0.9056405,0.002016785,0.000662003,0.0002295374,0.0141797,0.001639457,0.000199705,0.001861064,0.002123009,0.07081745],"study_design_scores_gemma":[0.00008251218,0.0002633647,0.9382316,0.007175907,0.0004908395,0.0005136984,0.02609699,0.004914158,0.0009582175,0.00126844,0.01987964,0.0001245494],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411338,0.01552343,0.00875334,0.007783688,0.000177815,0.0009859704,0.01948735,0.0001147843,0.006039686],"genre_scores_gemma":[0.9816041,0.002321496,0.007016572,0.0004221647,0.00004575081,0.0005130191,0.007821515,0.00003584445,0.0002195448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.105012,"threshold_uncertainty_score":0.5553631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1985422625943003,"score_gpt":0.4627275088317815,"score_spread":0.2641852462374813,"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."}}