{"id":"W4324027770","doi":"10.1101/2023.03.10.531570","title":"Optimizing Short-format Training: an International Consensus on Effective, Inclusive, and Career-spanning Professional Development in the Life Sciences and Beyond","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"CNIB Foundation; Queen's University","funders":"Cold Spring Harbor Laboratory; National Science Foundation","keywords":"Equity (law); Stakeholder; Medical education; Inclusion (mineral); Professional development; Political science; Engineering ethics; Psychology; Public relations; Sociology; Engineering; Medicine; Social science","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":[],"consensus_categories":[],"category_scores_codex":[0.1667468,0.0009790275,0.001626063,0.003776039,0.004501851,0.01381548,0.007591461,0.008758411,0.003392101],"category_scores_gemma":[0.1576543,0.0007111335,0.001453312,0.004315792,0.0163784,0.0136973,0.01586404,0.01432472,0.001086032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01191829,"about_ca_system_score_gemma":0.05986645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007672323,"about_ca_topic_score_gemma":0.009537243,"domain_scores_codex":[0.9119415,0.04732786,0.01160496,0.007302659,0.01820108,0.003621859],"domain_scores_gemma":[0.8213038,0.1105293,0.008977509,0.01150923,0.03839199,0.00928813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001172502,0.0003029667,0.0034663,0.01314931,0.0001512447,0.0001370281,0.04382915,0.001248136,0.001014601,0.1652316,0.06147014,0.7098823],"study_design_scores_gemma":[0.00009477039,0.0003258791,0.01065839,0.108192,0.0002848863,0.0003992517,0.06667803,0.001664879,0.002660781,0.1549951,0.6538219,0.0002241928],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.0227577,0.2302932,0.06274559,0.612955,0.01081837,0.0007280412,0.0002138186,0.0003433182,0.05914505],"genre_scores_gemma":[0.5076013,0.2005416,0.1394743,0.1397349,0.002576257,0.001596991,0.0004954992,0.0007392647,0.007239846],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1667468,"threshold_uncertainty_score":0.8818517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03123355144579698,"score_gpt":0.2604475186195894,"score_spread":0.2292139671737924,"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."}}