{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001270805,0.0003100325,0.0005977366,0.0005368365,0.001034054,0.000045583,0.000250441,0.000255967,0.00009960282],"category_scores_gemma":[0.0001027086,0.0002949491,0.0000597284,0.0008870443,0.00001420468,0.0003574512,0.00006009183,0.0008538736,0.0006475035],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.005060089,"about_ca_system_score_gemma":0.01269146,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01752021,"about_ca_topic_score_gemma":0.005488215,"domain_scores_codex":[0.9955019,0.0007969054,0.001537648,0.0003845424,0.0004433498,0.001335704],"domain_scores_gemma":[0.9970461,0.0001956822,0.0007404388,0.0008527422,0.0002143144,0.0009507517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007037696,0.0003479019,0.03851544,0.0009325338,0.00001806149,0.000006548963,0.5970005,0.00003544647,0.000007134206,0.0002481972,0.1970475,0.1657704],"study_design_scores_gemma":[0.001772309,0.001343762,0.003954422,0.003296111,0.00001516971,0.0003541664,0.6310809,0.0004457476,0.000001820327,0.0002565066,0.3570782,0.0004009315],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631911,0.0002476306,0.0001107471,0.01396834,0.007236105,0.0110102,0.00000617352,0.0002699815,0.003959731],"genre_scores_gemma":[0.9680896,0.00001408369,0.001940674,0.004888315,0.005325547,0.000619464,0.00003295046,0.0001008696,0.01898847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1653694,"threshold_uncertainty_score":0.9999503,"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."}}