{"id":"W2155058883","doi":"10.1111/1475-6773.00143","title":"Developing and Testing a Model to Predict Outcomes of Organizational Change","year":2003,"lang":"en","type":"article","venue":"Health Services Research","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":311,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agency for Healthcare Research and Quality","keywords":"Logistic regression; Odds; Outcome (game theory); Health care; Statistics; Bayesian probability; Computer science; Medicine; Machine learning; Artificial intelligence; Mathematics","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":[0.04613668,0.001640183,0.0012184,0.003311404,0.0007400319,0.0024102,0.001779582,0.002063608,0.002253321],"category_scores_gemma":[0.1328626,0.0008473869,0.001636862,0.001718436,0.001365331,0.002832409,0.00178138,0.001433219,0.0003928158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002975195,"about_ca_system_score_gemma":0.003809885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01176923,"about_ca_topic_score_gemma":0.006519895,"domain_scores_codex":[0.9767885,0.01825219,0.0007513193,0.001960926,0.001722546,0.0005245275],"domain_scores_gemma":[0.8507726,0.1343905,0.00663952,0.002267854,0.005051627,0.0008779123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001380991,0.001822915,0.2876057,0.000573442,0.0009161297,0.0002133996,0.00136908,0.6045177,0.0007555573,0.01492776,0.001680899,0.08423648],"study_design_scores_gemma":[0.0001734465,0.0007663958,0.0167843,0.0001233725,0.0001025785,0.0000475701,0.0001886326,0.9748437,0.0002890485,0.006215122,0.0004211908,0.00004470187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.630761,0.0002940112,0.3617139,0.00139121,0.00004983684,0.00130148,0.0006484307,0.0003922279,0.003447889],"genre_scores_gemma":[0.8715898,0.0001442541,0.1255508,0.0001613372,0.00003067198,0.001361602,0.0006944923,0.00002631633,0.000440886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04613668,"threshold_uncertainty_score":0.243997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8674206180880245,"score_gpt":0.7345714569153845,"score_spread":0.13284916117264,"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."}}