{"id":"W4285388235","doi":"10.7202/1090531ar","title":"Méthode d’opérationnalisation de mesures de la performance sensibles aux soins infirmiers basées sur des données de routine","year":2022,"lang":"en","type":"article","venue":"Science of Nursing and Health Practices","topic":"Healthcare Systems and Practices","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Operationalization; Computer science; Argumentation theory; Measure (data warehouse); Relevance (law); Process (computing); Nursing research; Psychology; Process management; Nursing; Medicine; Data mining; Epistemology; Political science; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.03896764,0.0001452979,0.0002944689,0.0003147215,0.008590559,0.00009126984,0.0002415648,0.0001020756,0.00005421943],"category_scores_gemma":[0.003526777,0.0001373682,0.00002898808,0.0007244311,0.0008174628,0.001583113,0.00009914597,0.0008842815,0.000001772751],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009601673,"about_ca_system_score_gemma":0.01258013,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01668378,"about_ca_topic_score_gemma":0.0009397289,"domain_scores_codex":[0.9914488,0.005999723,0.0006814657,0.0003515174,0.0006067894,0.0009116883],"domain_scores_gemma":[0.9944404,0.002730884,0.001884379,0.0002378726,0.0003248704,0.0003816041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000262445,0.0003551269,0.7012389,0.00322384,0.00002244321,0.000005865445,0.1843694,0.001357475,0.007094327,0.03199501,0.004022142,0.06605306],"study_design_scores_gemma":[0.0009820976,0.001006553,0.8029611,0.002396251,0.0000515126,0.0002499454,0.1340588,0.01572755,0.0004533418,0.01014107,0.03162351,0.0003482853],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9530407,0.001363742,0.0005129846,0.03868101,0.0003739088,0.0004217912,0.00001375394,0.0000693992,0.005522669],"genre_scores_gemma":[0.9879639,0.001139604,0.009113134,0.001318863,0.0001987522,0.00005426958,0.000004542861,0.00001630086,0.0001907036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1017222,"threshold_uncertainty_score":0.9930176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4874646037653567,"score_gpt":0.5844006288809658,"score_spread":0.09693602511560906,"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."}}