{"id":"W4366830828","doi":"10.1017/cts.2023.179","title":"96 Evidence to impact: Developing a workforce of translational research professionals","year":2023,"lang":"en","type":"article","venue":"Journal of Clinical and Translational Science","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mentorship; Curriculum; Workforce; Translational science; Medical education; Translational research; Health care; Workforce development; Medicine; Multidisciplinary approach; Capstone; Knowledge management; Psychology; Engineering; Pedagogy; Computer science; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2417101,0.001013599,0.00130845,0.003970455,0.005653769,0.01915677,0.006796849,0.008743442,0.02415176],"category_scores_gemma":[0.3666159,0.001319487,0.001848143,0.00295993,0.005433343,0.01270377,0.02045509,0.008923732,0.007519056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01827145,"about_ca_system_score_gemma":0.2564409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01546177,"about_ca_topic_score_gemma":0.02698477,"domain_scores_codex":[0.8394428,0.110655,0.01198961,0.004573875,0.02591366,0.007424883],"domain_scores_gemma":[0.6336898,0.1423017,0.02368097,0.0258798,0.1049348,0.06951289],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004640252,0.001154406,0.02300503,0.02173193,0.0004848744,0.0008359777,0.007040269,0.0008670339,0.001368811,0.03624492,0.2396484,0.6671544],"study_design_scores_gemma":[0.00146215,0.001862684,0.02891083,0.1104585,0.0008631581,0.0007108244,0.02731143,0.001109276,0.00169203,0.04002651,0.785402,0.0001905528],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01408073,0.0346323,0.01914115,0.8082636,0.01589254,0.007620405,0.00101105,0.0006042456,0.09875409],"genre_scores_gemma":[0.2841259,0.08367316,0.2446084,0.3299946,0.007072804,0.02085977,0.004203343,0.0006666397,0.02479541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7582899,"threshold_uncertainty_score":0.9351065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7791536635515284,"score_gpt":0.7100424267624076,"score_spread":0.06911123678912079,"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."}}