{"id":"W4282914863","doi":"10.2196/30537","title":"Harnessing Natural Language Processing to Support Decisions Around Workplace-Based Assessment: Machine Learning Study of Competency-Based Medical Education","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Innovations in Medical Education","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Trigram; Artificial intelligence; Computer science; Competence (human resources); Narrative; Machine learning; Natural language processing; Qualitative property; Binary classification; Psychology; Social psychology; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[],"category_scores_codex":[0.008519463,0.0004626763,0.0002908594,0.001520926,0.0004308476,0.001385747,0.0008016893,0.000614262,0.0009175718],"category_scores_gemma":[0.06214485,0.0001863803,0.0004455111,0.0009884636,0.0008772689,0.00184114,0.00072273,0.001465077,0.000296817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009128259,"about_ca_system_score_gemma":0.001047785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005373718,"about_ca_topic_score_gemma":0.005362955,"domain_scores_codex":[0.9950382,0.003617949,0.0002178648,0.0005996482,0.0003923147,0.0001340092],"domain_scores_gemma":[0.8940515,0.09671388,0.003459627,0.001677183,0.003330112,0.0007677571],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006922917,0.003068227,0.5179597,0.000473357,0.0002589833,0.0006100456,0.00683355,0.02720836,0.006479103,0.0022156,0.002388487,0.4318123],"study_design_scores_gemma":[0.00007630507,0.001342532,0.2984983,0.000174049,0.0001080995,0.0005422762,0.005147264,0.6764113,0.007168212,0.008093435,0.002342053,0.00009616325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724779,0.0003242275,0.02469789,0.0007400468,0.00002106768,0.0001514807,0.0002032718,0.00008047774,0.001303626],"genre_scores_gemma":[0.982443,0.0001163256,0.01672303,0.00008658021,0.0000284673,0.00006075556,0.0002170969,0.000009020298,0.0003157648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9914805,"threshold_uncertainty_score":0.04505575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01366462923885893,"score_gpt":0.400859934277861,"score_spread":0.3871953050390021,"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."}}