{"id":"W3048799016","doi":"10.23889/ijpds.v5i1.1340","title":"Development of comparable algorithms to measure primary care indicators using administrative health data across three Canadian provinces","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montfort Hospital; Bruyère; University of Ottawa; Dalhousie University; University of British Columbia; Western University; University of Waterloo","funders":"Canadian Institutes of Health Research; Health Canada; Michael Smith Health Research BC; Dalhousie University; Nova Scotia Department of Health and Wellness; Ontario Ministry of Health and Long-Term Care; Department of Health, Western Cape Government","keywords":"Comparability; Performance indicator; Health care; Work (physics); Health indicator; Computer science; Business; Political science; Engineering; Marketing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0025835,0.0001328582,0.0002919165,0.0002912468,0.002209752,0.00009299847,0.003505657,0.00005748619,0.00003308856],"category_scores_gemma":[0.0005898447,0.0001188773,0.00002300133,0.0005314199,0.00008750936,0.001800036,0.001056669,0.0003460263,0.000009240978],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002854261,"about_ca_system_score_gemma":0.03455444,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02185611,"about_ca_topic_score_gemma":0.1954512,"domain_scores_codex":[0.9965909,0.0000815577,0.001057144,0.0005123407,0.00118942,0.0005686496],"domain_scores_gemma":[0.9969407,0.0001044313,0.0007873526,0.0004947162,0.0008455425,0.0008272047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004123523,0.00006854689,0.6119719,0.0007024133,0.0001501628,0.00001422348,0.05332249,0.0003216444,0.0002797802,0.001340918,0.02300358,0.308412],"study_design_scores_gemma":[0.001619899,0.0002191815,0.6124887,0.000726686,0.00002384788,0.0000209519,0.01691745,0.01073652,0.00005919646,0.0001321992,0.3565491,0.0005062812],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7213333,0.001105804,0.143014,0.07177219,0.01270406,0.006279444,0.04188953,0.0001254229,0.001776352],"genre_scores_gemma":[0.8199959,0.00001494895,0.1623545,0.01250429,0.0006247351,0.00001527791,0.00445358,0.00001846979,0.00001827251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3335456,"threshold_uncertainty_score":0.9990892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5058754779513256,"score_gpt":0.5708277109988958,"score_spread":0.06495223304757025,"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."}}