{"id":"W4409648472","doi":"10.22541/au.174522534.41721038/v1","title":"Depriving the Deprived: Examining Brain Drains of Health Professionals from Low- and Middle-Income Countries (LMICs) to Wealthy Nations with an Equity Lens","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Equity (law); Low and middle income countries; Brain drain; Health professionals; Lens (geology); Through-the-lens metering; Business; Economic growth; Optometry; Political science; Developing country; Economics; Medicine; Biology; Health care; Law","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":[0.003172327,0.0004936904,0.0005737563,0.003872465,0.003630937,0.004622746,0.001219247,0.00108198,0.003788465],"category_scores_gemma":[0.01529957,0.0002510317,0.0006364557,0.003954632,0.00576613,0.008628689,0.01401247,0.002431981,0.0002731736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00550151,"about_ca_system_score_gemma":0.007281318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02458606,"about_ca_topic_score_gemma":0.04212959,"domain_scores_codex":[0.9965261,0.001514324,0.0002006942,0.0001551797,0.0004905344,0.001113257],"domain_scores_gemma":[0.9929489,0.002608543,0.00177421,0.0001616516,0.001025008,0.00148177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002702939,0.0005510633,0.582881,0.0020347,0.0003173295,0.001357244,0.250834,0.0003839792,0.0003312987,0.03347612,0.01338511,0.1141779],"study_design_scores_gemma":[0.00002695428,0.0002991552,0.3407662,0.002922544,0.0001338616,0.0005564021,0.6182573,0.0004725355,0.0002391902,0.01008322,0.02619963,0.00004307282],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9523926,0.006598241,0.0005192088,0.02205102,0.0001919718,0.0001000163,0.000479616,0.000005581358,0.01766164],"genre_scores_gemma":[0.9916239,0.003210963,0.0002780118,0.003589638,0.0001169118,0.000104957,0.0001773507,0.000007413752,0.000890805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02458606,"threshold_uncertainty_score":0.04888588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1083528902542888,"score_gpt":0.4631747835776774,"score_spread":0.3548218933233886,"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."}}