{"id":"W6976849010","doi":"10.6084/m9.figshare.12032961.v1","title":"Additional file 1 of “We’ve got the home care data, what do we do with it?”: understanding data use in decision making and quality improvement","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quality (philosophy); Quality management; Data collection; Data quality; Work (physics)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005843339,0.000526946,0.0005668206,0.002028496,0.00133353,0.001346958,0.001228042,0.00103775,0.843679],"category_scores_gemma":[0.06714982,0.0004190477,0.0004571272,0.004427478,0.0003171288,0.002855956,0.0014351,0.001283616,0.1381904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001916055,"about_ca_system_score_gemma":0.003768348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01388242,"about_ca_topic_score_gemma":0.02227497,"domain_scores_codex":[0.9978511,0.001161061,0.0002748316,0.0001858522,0.0003241014,0.0002030439],"domain_scores_gemma":[0.9282697,0.06060081,0.001794646,0.001542117,0.006901617,0.0008910083],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005768957,0.00003414091,0.0004797752,0.0008610535,0.000004411041,0.00001712639,0.0005123489,0.00008889985,0.00001485279,0.0008352038,0.9913583,0.00573631],"study_design_scores_gemma":[0.0008501207,0.0001030427,0.01042644,0.003764689,0.00003638641,0.0001282459,0.006002409,0.0006314687,0.0002321101,0.009371545,0.9683741,0.00007951797],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0006981363,0.0000484275,0.001485173,0.001639802,0.00009434021,0.001245522,0.9817722,0.0003316169,0.01268479],"genre_scores_gemma":[0.0404917,0.0008874578,0.02529189,0.006081441,0.0003782823,0.05486431,0.7783844,0.002292448,0.09132807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9941567,"threshold_uncertainty_score":0.2229729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.164957640532669,"score_gpt":0.290106904998837,"score_spread":0.125149264466168,"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."}}