{"id":"W2567215800","doi":"10.1016/j.jclinepi.2016.12.006","title":"Diffusion of Innovations model helps interpret the comparative uptake of two methodological innovations: co-authorship network analysis and recommendations for the integration of novel methods in practice","year":2016,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; Ontario Ministry of Research and Innovation; McGill University","keywords":"Diffusion of innovations; Computer science; Opinion leadership; Diffusion; Early adopter; Knowledge translation; Confounding; Data science; Medicine; Knowledge management; Sociology; Political science; Public relations; Social science; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2074311,0.000133013,0.001953369,0.0004512689,0.0002620333,0.000002553361,0.0004325864,0.0002668981,0.00005208565],"category_scores_gemma":[0.4392066,0.00006029364,0.0002704814,0.002069181,0.000753116,0.0002615815,0.0001255402,0.001067546,3.896048e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000667061,"about_ca_system_score_gemma":0.0007570497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001581367,"about_ca_topic_score_gemma":0.0003675614,"domain_scores_codex":[0.9457092,0.03965598,0.01362575,0.0002942776,0.0002782122,0.0004365995],"domain_scores_gemma":[0.4609663,0.5258008,0.01030654,0.0003372841,0.002493412,0.00009563017],"domain_codex":null,"domain_gemma":"methods","domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.002053889,0.0005478424,0.2508554,0.00009647122,0.001043325,1.371108e-7,0.01572561,0.007361052,0.005427607,0.6339642,0.009129429,0.07379497],"study_design_scores_gemma":[0.00601584,0.001487925,0.5783293,0.0007213405,0.0009520355,0.000008511716,0.01259311,0.2232032,0.0002067143,0.1657439,0.01053389,0.0002042751],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07784146,0.0000393371,0.792041,0.1290015,0.0003097604,0.0006087639,0.00008617183,0.000002658816,0.00006938302],"genre_scores_gemma":[0.4030714,0.0003087891,0.5884424,0.00796039,0.0001300745,0.00006010252,0.000005125778,0.000004421547,0.00001737101],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4861448,"threshold_uncertainty_score":0.8161165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9632085998666559,"score_gpt":0.8302669423984486,"score_spread":0.1329416574682073,"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."}}