{"id":"W2990105953","doi":"10.2196/16186","title":"Education Into Policy: Embedding Health Informatics to Prepare Future Nurses—New Zealand Case Study","year":2019,"lang":"en","type":"article","venue":"JMIR Nursing","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health informatics; Informatics; Workforce; Health Administration Informatics; Nursing; Health care; Medicine; Medical education; Nurse education; Political science; Public health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.006911948,0.0003228434,0.0002285775,0.0008180302,0.008237453,0.003579261,0.001303136,0.003853575,0.00315984],"category_scores_gemma":[0.01478785,0.0003729444,0.0003528107,0.001088514,0.005362027,0.002952332,0.003880801,0.003112433,0.0003406129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01690459,"about_ca_system_score_gemma":0.01746956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1776038,"about_ca_topic_score_gemma":0.287854,"domain_scores_codex":[0.9948294,0.003271189,0.0002086619,0.000191309,0.0007003006,0.0007992279],"domain_scores_gemma":[0.9949109,0.002688344,0.0004781591,0.0002539554,0.0005197583,0.001148898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001430884,0.001481399,0.02728954,0.0007532626,0.00001810205,0.05351666,0.7367091,0.00154086,0.001834673,0.07287373,0.02090072,0.08293886],"study_design_scores_gemma":[0.0001299283,0.000538068,0.01737913,0.0009324303,0.00002604848,0.01001771,0.6825044,0.002420746,0.001064732,0.007166502,0.2777387,0.0000815057],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7967855,0.001465313,0.005585316,0.06961006,0.0003826275,0.001168158,0.00007402129,0.00004011918,0.1248889],"genre_scores_gemma":[0.9590741,0.002701357,0.01179699,0.003945602,0.0000692405,0.0003934605,0.00003623304,0.00002285983,0.02196016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1776038,"threshold_uncertainty_score":0.3531401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.027293572739248,"score_gpt":0.519869610674658,"score_spread":0.49257603793541,"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."}}