{"id":"W3043841497","doi":"10.1093/milmed/usaa184","title":"MHS Genesis Implementation: Strategies in Support of Successful EHR Conversion","year":2020,"lang":"en","type":"article","venue":"Military Medicine","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Inclusion (mineral); Leverage (statistics); Medical education; Public relations; Medicine; Psychology; Political science; Computer science; Sociology; Social science","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":[0.02930392,0.0007330495,0.0006029087,0.005004874,0.003106786,0.009737166,0.002588187,0.002816833,0.007614545],"category_scores_gemma":[0.06920216,0.0004757936,0.001319067,0.003838798,0.002180098,0.009400171,0.008270252,0.002424264,0.001573851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006434541,"about_ca_system_score_gemma":0.02690834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004357878,"about_ca_topic_score_gemma":0.01030214,"domain_scores_codex":[0.9803193,0.01350968,0.00171036,0.0007942854,0.002368536,0.001297702],"domain_scores_gemma":[0.9656241,0.02262667,0.003364018,0.001489446,0.004878247,0.002017607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001941962,0.0008027757,0.02063255,0.01545292,0.0002107821,0.001500556,0.05490999,0.0005148946,0.001489077,0.02955178,0.0252108,0.8495296],"study_design_scores_gemma":[0.0004787593,0.0021124,0.06753194,0.05143464,0.001112087,0.002832746,0.2584091,0.003923498,0.008690323,0.03709711,0.5661644,0.0002128943],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3056131,0.05705571,0.07417807,0.2405193,0.002514511,0.01295462,0.0007896216,0.002102576,0.3042724],"genre_scores_gemma":[0.8376518,0.02400584,0.1123895,0.01059895,0.0003590006,0.003093813,0.0003923256,0.0001440567,0.01136471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02930392,"threshold_uncertainty_score":0.1549758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3984415983299208,"score_gpt":0.6136052130528691,"score_spread":0.2151636147229483,"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."}}