{"id":"W3177058445","doi":"10.1016/j.hjdsi.2020.100479","title":"Accelerating learning healthcare system development through embedded research: Career trajectories, training needs, and strategies for managing and supporting embedded researchers","year":2021,"lang":"en","type":"article","venue":"Healthcare","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mailman School of Public Health, Columbia University; Agency for Healthcare Research and Quality; McGill University; Northwestern University; AcademyHealth; Health Services Research and Development; University of California, Santa Barbara; U.S. Department of Veterans Affairs","keywords":"Workforce; Workforce development; Brainstorming; Knowledge management; Stakeholder; Health care; Curriculum; Medical education; Psychology; Public relations; Business; Computer science; Medicine; Political science; Pedagogy; Marketing","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":[0.09146651,0.0007007004,0.0005640303,0.00179297,0.01265451,0.01474638,0.003886333,0.004511996,0.006861006],"category_scores_gemma":[0.0916682,0.0007876974,0.00087258,0.001381295,0.005793316,0.01524629,0.02209471,0.007543688,0.001548684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006414696,"about_ca_system_score_gemma":0.0739729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003722989,"about_ca_topic_score_gemma":0.009748345,"domain_scores_codex":[0.9497068,0.03680517,0.001604404,0.001771032,0.003074892,0.007037833],"domain_scores_gemma":[0.8728852,0.05541987,0.008549274,0.006359671,0.01065795,0.04612814],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003322056,0.002045476,0.05702191,0.004070289,0.00008546207,0.00122446,0.3003902,0.0008849956,0.002101402,0.0299659,0.03656276,0.565315],"study_design_scores_gemma":[0.0001837716,0.001276694,0.0293208,0.008295329,0.00009045169,0.001193697,0.7411249,0.003217362,0.002265085,0.07762798,0.1352182,0.0001857205],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3353617,0.01361695,0.02997193,0.5877163,0.001841574,0.001274092,0.0001334286,0.0004134637,0.02967061],"genre_scores_gemma":[0.8889338,0.01058034,0.07217217,0.01993252,0.0003660755,0.00163116,0.0002382227,0.0001054693,0.006040116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9085335,"threshold_uncertainty_score":0.4837269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8879858232217707,"score_gpt":0.6797529374871581,"score_spread":0.2082328857346125,"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."}}