{"id":"W4387976072","doi":"10.1101/2023.10.26.23297605","title":"Mapping the landscape: A protocol of a jurisdictional scan of self-identified learning health systems","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Public Health Ontario; University of Toronto; Trillium Health Centre","funders":"","keywords":"CINAHL; MEDLINE; Grey literature; Modalities; Health care; Knowledge management; Context (archaeology); Benchmarking; Computer science; Analytics; Data science; Business; Political science; Sociology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2035212,0.00332028,0.007461162,0.02416687,0.007615014,0.008429777,0.008597475,0.009180044,0.0699665],"category_scores_gemma":[0.2575511,0.004242107,0.009450934,0.01866337,0.006326397,0.01132079,0.01089538,0.007068884,0.01245331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01632504,"about_ca_system_score_gemma":0.09464177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0157757,"about_ca_topic_score_gemma":0.02582867,"domain_scores_codex":[0.8373684,0.0994866,0.03591237,0.008925538,0.0128753,0.005431713],"domain_scores_gemma":[0.7822251,0.08086966,0.01506547,0.03363759,0.08391855,0.004283598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01004483,0.003072902,0.008868097,0.2915411,0.001893458,0.007687324,0.131475,0.006597552,0.004441448,0.07297502,0.2036516,0.2577515],"study_design_scores_gemma":[0.00952169,0.001975607,0.01495785,0.1523635,0.00106525,0.001290238,0.0478374,0.003387492,0.003111919,0.03661652,0.7271559,0.0007166305],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.001524884,0.0005924755,0.007629962,0.0008782957,0.0002893888,0.9815761,0.004642694,0.0001103741,0.002755882],"genre_scores_gemma":[0.001692487,0.0002257397,0.01092755,0.0003511135,0.00002783421,0.9856347,0.0007461053,0.00002205936,0.0003724455],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.2035212,"threshold_uncertainty_score":0.9822001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5106374495712585,"score_gpt":0.6031851171066327,"score_spread":0.09254766753537425,"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."}}