{"id":"W2111169914","doi":"10.12927/hcq.2015.24122","title":"Measuring Maturity of Use for Electronic Medical Records in British Columbia: The Physician Information Technology Office","year":2014,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"CARE Canada; Canada Health Infoway","funders":"","keywords":"Maturity (psychological); Medical record; Health information technology; Best practice; Capability Maturity Model; Electronic medical record; Information technology; Business; Medicine; Family medicine; Psychology; Health care; Management; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004863734,0.0001575272,0.0006371022,0.0001835154,0.0006764811,0.00004369769,0.0004720364,0.0007580699,0.00003631277],"category_scores_gemma":[0.001106002,0.0001916146,0.00008868692,0.0007087243,0.00009857062,0.0004353042,0.00003185001,0.002017936,0.00004792085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009136854,"about_ca_system_score_gemma":0.003553239,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1663966,"about_ca_topic_score_gemma":0.884142,"domain_scores_codex":[0.9942474,0.001546762,0.001747216,0.0003189904,0.0005671659,0.0015725],"domain_scores_gemma":[0.9963191,0.001506452,0.0007691856,0.0005992897,0.0006032231,0.0002027716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001797385,0.0001568884,0.1236575,0.006572292,0.00004022657,0.000002015939,0.005248575,0.000001474635,0.0000263585,0.01152408,0.01330049,0.8392903],"study_design_scores_gemma":[0.0077526,0.008852797,0.3217093,0.007209631,0.00004407274,0.00008339487,0.02207565,0.007465162,0.000008542921,0.02279067,0.6010103,0.0009978994],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739484,0.0004519952,0.002658507,0.0168266,0.001177505,0.004365345,0.00006010587,0.000225996,0.0002855987],"genre_scores_gemma":[0.9940901,0.0000943189,0.0001472669,0.00386659,0.0003909976,0.00120931,0.00004391759,0.0000363793,0.0001210792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8382924,"threshold_uncertainty_score":0.8767042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0309328510685919,"score_gpt":0.3408670762432227,"score_spread":0.3099342251746308,"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."}}