{"id":"W4214900227","doi":"10.23889/ijpds.v7i1.1689","title":"Describing the linkage between administrative social assistance and health care databases in Ontario, Canada","year":2022,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Children, Community and Social Services; Hospital for Sick Children; Public Health Ontario; Trillium Health Centre; SickKids Foundation; University of Toronto; Institute for Clinical Evaluative Sciences; Centre for Addiction and Mental Health","funders":"","keywords":"Record linkage; Database; Linkage (software); Representativeness heuristic; Population; Service (business); Medicine; Christian ministry; Business; Computer science; Environmental health; Psychology; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01826051,0.0003631053,0.0005193888,0.009034894,0.004679976,0.005529228,0.002307039,0.0007038416,0.002540732],"category_scores_gemma":[0.05782708,0.000556253,0.0008324534,0.03678154,0.001001462,0.001770316,0.003404,0.0005643465,0.0005738055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08949792,"about_ca_system_score_gemma":0.1941205,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9920651,"about_ca_topic_score_gemma":0.9908054,"domain_scores_codex":[0.9811174,0.003237203,0.002943273,0.001609015,0.009338694,0.001754427],"domain_scores_gemma":[0.9489992,0.01039226,0.006635185,0.00337746,0.02957671,0.001019255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003254502,0.0001022911,0.593022,0.002745838,0.0006149595,0.0009155666,0.01605886,0.02141813,0.001330846,0.03390935,0.1156864,0.2138704],"study_design_scores_gemma":[0.0001046752,0.00006673324,0.5918908,0.002615337,0.0003415556,0.0003355651,0.01322242,0.02988715,0.002160029,0.005471625,0.3536498,0.0002542729],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.4002836,0.01218069,0.07545147,0.01639051,0.0003297183,0.006010191,0.4107688,0.002115366,0.07646956],"genre_scores_gemma":[0.6779079,0.008421907,0.117832,0.001298948,0.0000868339,0.003179733,0.1759326,0.0003056308,0.0150344],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.08949792,"threshold_uncertainty_score":0.6493559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.658627486546011,"score_gpt":0.5247302681115205,"score_spread":0.1338972184344905,"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."}}