{"id":"W3217292613","doi":"10.2196/30831","title":"Medical Brain Drain From Southeastern Europe: Using Digital Demography to Forecast Health Worker Emigration","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emigration; Pandemic; Geography; Demography; Test (biology); Demographic economics; Coronavirus disease 2019 (COVID-19); Medicine; Sociology; Economics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001015506,0.000296366,0.0005647637,0.0001532621,0.0007523213,0.00006531567,0.000309619,0.0003475169,0.001364413],"category_scores_gemma":[0.001809703,0.000273731,0.0001214207,0.001283107,0.00006498254,0.0002152483,0.0003099556,0.0008209592,0.0008414769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001963748,"about_ca_system_score_gemma":0.001577849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001039153,"about_ca_topic_score_gemma":0.006542346,"domain_scores_codex":[0.9949036,0.001133409,0.001115623,0.0006546389,0.0009914549,0.001201264],"domain_scores_gemma":[0.9965197,0.0008594346,0.0003194379,0.0005866509,0.0003747988,0.001339937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005393816,0.000438663,0.4966127,0.0004330742,0.0001456983,0.000286223,0.03924312,0.00005632572,0.0003191395,0.001399919,0.3543262,0.1061996],"study_design_scores_gemma":[0.002847457,0.0002360573,0.0453306,0.006070083,0.00002953211,0.00002316929,0.01749863,0.003008712,0.00003215335,0.001604468,0.9225454,0.0007737018],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8635845,0.0007822059,0.00740307,0.118913,0.001781909,0.001675844,0.0003199184,0.0003443937,0.005195138],"genre_scores_gemma":[0.929072,0.00004308851,0.004612134,0.05113943,0.00184098,0.0001595339,0.0006776954,0.0001434224,0.0123117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5682192,"threshold_uncertainty_score":0.9999715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05652189820961138,"score_gpt":0.4334917091585154,"score_spread":0.376969810948904,"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."}}