{"id":"W2835651434","doi":"10.1017/cts.2018.11","title":"Comprehensive strategy for capturing and integrating community input into community research training curricula","year":2018,"lang":"en","type":"article","venue":"Journal of Clinical and Translational Science","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; National Institutes of Health; National Center for Advancing Translational Sciences; Canada Excellence Research Chairs, Government of Canada","keywords":"Training (meteorology); Curriculum; Computer science; Medical education; Mathematics education; Sociology; Psychology; Pedagogy; Geography; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","research_integrity"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.05225848,0.00008707819,0.0003274102,0.0002219227,0.007635263,0.00005596994,0.000438528,0.00008936741,0.00002513514],"category_scores_gemma":[0.009441392,0.00006373089,0.00004849603,0.0005792946,0.003832669,0.0006409117,0.0001185922,0.003037162,0.000002452423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002910168,"about_ca_system_score_gemma":0.00179635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006157484,"about_ca_topic_score_gemma":0.0009550464,"domain_scores_codex":[0.9920929,0.00498147,0.001585572,0.0001419241,0.0007424529,0.0004556523],"domain_scores_gemma":[0.9678132,0.02849906,0.0005239889,0.0001407713,0.002565458,0.000457525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004649744,0.0002248898,0.2920832,0.0005837434,0.00003556133,0.000002412195,0.3881725,0.00003694374,0.002886661,0.02726863,0.0006356671,0.2876048],"study_design_scores_gemma":[0.002269025,0.001260534,0.8453035,0.0004454714,0.00001132186,0.00002033412,0.07350868,0.002543691,0.00004182642,0.06679422,0.00767538,0.0001259841],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875759,0.0000372519,0.004335327,0.006489174,0.0002819365,0.0003252828,0.00001326493,0.000006575463,0.000935233],"genre_scores_gemma":[0.9855865,0.00002799722,0.01210324,0.001806243,0.0004514187,0.000007725513,8.860999e-7,0.000004547723,0.00001148476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5532204,"threshold_uncertainty_score":0.9992629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9441107591526586,"score_gpt":0.7887347478772645,"score_spread":0.1553760112753941,"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."}}