{"id":"W7135298172","doi":"","title":"Reducing socioeconomic health differencesbetween 2000 and 2020: Monitoring setup","year":2003,"lang":"nl","type":"report","venue":"Rivm (National Institute for Public Health and the Environment)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Socioeconomic status; Government (linguistics); Psychological intervention; Quarter (Canadian coin); Socioeconomic development; Health care; Public health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006083265,0.0008496746,0.0003858691,0.002959957,0.0008512796,0.001060037,0.001488988,0.0008326343,0.004718082],"category_scores_gemma":[0.01185198,0.0006971901,0.0006063935,0.004400102,0.0003073141,0.001438901,0.002465394,0.000784863,0.00143669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004214661,"about_ca_system_score_gemma":0.004172538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1044184,"about_ca_topic_score_gemma":0.08082443,"domain_scores_codex":[0.9963175,0.001257161,0.0005810683,0.0005659693,0.000799145,0.0004791993],"domain_scores_gemma":[0.9921883,0.0007939988,0.002207915,0.0008014987,0.002991571,0.00101662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009754945,0.00173235,0.8351459,0.0005077942,0.0001790686,0.0003283245,0.003080935,0.003670407,0.001422482,0.001308939,0.05402578,0.09762256],"study_design_scores_gemma":[0.0001123218,0.000452078,0.9708098,0.00007706234,0.00004400078,0.00008860514,0.00125807,0.002859286,0.0009164539,0.0002570693,0.02308859,0.00003662454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6896283,0.0006466692,0.009622754,0.002498311,0.0002070972,0.01482092,0.2524952,0.0006188762,0.02946188],"genre_scores_gemma":[0.6697446,0.0009348639,0.02594405,0.0004761918,0.000166329,0.04140851,0.2486705,0.0001578719,0.01249707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1044184,"threshold_uncertainty_score":0.2076212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07688475314171185,"score_gpt":0.3173024864364773,"score_spread":0.2404177332947655,"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."}}