{"id":"W3214764977","doi":"10.2196/34078","title":"Peer Review of “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":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emigration; Brain drain; Geography; Demographic economics; Demography; Sociology; Economics; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02294885,0.0006472453,0.001326277,0.003783178,0.00409793,0.006395016,0.002482035,0.005190021,0.0597361],"category_scores_gemma":[0.2584994,0.0004026214,0.001354787,0.001907011,0.002174353,0.003141487,0.003093241,0.00288535,0.02640439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001729283,"about_ca_system_score_gemma":0.01289781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005200097,"about_ca_topic_score_gemma":0.008375886,"domain_scores_codex":[0.9824345,0.005484061,0.002178503,0.0010056,0.008095817,0.000801612],"domain_scores_gemma":[0.5662197,0.03728183,0.009271048,0.008112339,0.3685176,0.01059741],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005011019,0.00001651274,0.0009768916,0.0005890816,0.00003161477,0.0001161924,0.0001620152,0.00003704218,0.0001522531,0.0003458863,0.9814283,0.01609405],"study_design_scores_gemma":[0.00008666152,0.00005756848,0.006170233,0.002214237,0.00008392589,0.000246985,0.001197971,0.0009054458,0.0007325544,0.001719291,0.986532,0.00005307444],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.006261962,0.004154272,0.003194014,0.3587365,0.5914235,0.001250512,0.003647756,0.000688683,0.03064288],"genre_scores_gemma":[0.1111097,0.0198045,0.0107734,0.1372868,0.4779299,0.00270394,0.01135849,0.002554773,0.2264787],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9770511,"threshold_uncertainty_score":0.1998371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08610704826801965,"score_gpt":0.4680585041347564,"score_spread":0.3819514558667367,"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."}}