{"id":"W2983865285","doi":"10.3389/fgene.2019.01057","title":"Ensuring Best Practice in Genomic Education and Evaluation: A Program Logic Approach","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; State Government of Victoria; University of Melbourne; Macquarie University; Murdoch Children's Research Institute; Medical Research Council; Children’s Hospital of Wisconsin Research Institute; University of Ottawa","keywords":"Process (computing); Computer science; Identification (biology); Genomics; Relevance (law); Logic model; Knowledge management; Process management; Data science; Engineering; Genome; Political science; Biology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.5429507,0.001996187,0.002211142,0.01264704,0.007873938,0.02629112,0.009120759,0.01116088,0.003971787],"category_scores_gemma":[0.4018198,0.002006107,0.00259152,0.008002128,0.03759428,0.02105688,0.01615052,0.01448734,0.001221058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05670134,"about_ca_system_score_gemma":0.1538302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009716622,"about_ca_topic_score_gemma":0.0108416,"domain_scores_codex":[0.3920966,0.5277407,0.01976841,0.01461289,0.03924864,0.006532659],"domain_scores_gemma":[0.3944545,0.4883782,0.02335525,0.02975089,0.05518218,0.008878988],"domain_codex":"methods","domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001473594,0.0007641009,0.003373137,0.0033759,0.0002855838,0.0002446446,0.01640768,0.0149548,0.0008543474,0.7377664,0.008408502,0.2134176],"study_design_scores_gemma":[0.0004087805,0.0007040121,0.001785562,0.006697328,0.0001869528,0.0001346846,0.008591047,0.02124782,0.002489386,0.9046807,0.05285664,0.0002170383],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008096536,0.001642729,0.8426829,0.1099704,0.0003881386,0.006535345,0.0001286765,0.0004931474,0.03006213],"genre_scores_gemma":[0.1387051,0.0009451615,0.8405669,0.008721406,0.0001282456,0.009266972,0.00009826658,0.0001228321,0.001445072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5429507,"threshold_uncertainty_score":0.5636232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.336052116352987,"score_gpt":0.6245930447612147,"score_spread":0.2885409284082276,"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."}}