{"id":"W4281262690","doi":"10.1002/jgc4.1589","title":"Integrating genetic assistants into the workforce: An 18‐year productivity analysis and development of a staff mix planning tool","year":2022,"lang":"en","type":"article","venue":"Journal of Genetic Counseling","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"George & Fay Yee Centre for Healthcare Innovation; University of Manitoba; Thunder Bay Regional Health Sciences Centre","funders":"","keywords":"Workforce; Productivity; Workforce development; Public health; Workforce planning; Medicine; Skill mix; Medical education; Business; Nursing; Operations management; Health care; Engineering; Economic growth; Economics","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":[0.007662205,0.0009929314,0.0005580949,0.006302444,0.0005325883,0.001955374,0.001264614,0.0005608494,0.003467924],"category_scores_gemma":[0.01754161,0.0004932673,0.001057959,0.003077618,0.0001828083,0.001439471,0.001636789,0.0008660664,0.0005716232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00235069,"about_ca_system_score_gemma":0.002441801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01358903,"about_ca_topic_score_gemma":0.01399979,"domain_scores_codex":[0.998276,0.0007529796,0.0001751704,0.0002073563,0.0004118159,0.0001765721],"domain_scores_gemma":[0.9875298,0.007540965,0.002204153,0.0004170975,0.001745284,0.0005628837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004183335,0.002158124,0.6312208,0.0003183848,0.000323675,0.00057287,0.004015268,0.09447224,0.001530106,0.002997333,0.01084885,0.2511241],"study_design_scores_gemma":[0.0001298121,0.001186896,0.3404435,0.0002197896,0.000155425,0.0002522224,0.00585156,0.6316921,0.002434804,0.003028095,0.01442225,0.0001835975],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9066355,0.0001488232,0.0771444,0.0007024852,0.0000544717,0.0008895927,0.007462239,0.001296844,0.00566565],"genre_scores_gemma":[0.8173673,0.000194104,0.1734927,0.00007626303,0.00003085587,0.0009496344,0.006075782,0.0001275453,0.0016858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01358903,"threshold_uncertainty_score":0.0405221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05507839825025221,"score_gpt":0.3982405595119963,"score_spread":0.343162161261744,"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."}}