{"id":"W2956129393","doi":"10.1186/s12960-019-0381-5","title":"The early retiree divests the health workforce: a quantitative analysis of early retirement among Canadian Registered Nurses and allied health professionals","year":2019,"lang":"en","type":"article","venue":"Human Resources for Health","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; University of Alberta","funders":"University of Alberta","keywords":"Workforce; Logistic regression; Aging in the American workforce; Variance (accounting); Retirement age; Gerontology; Test (biology); Restructuring; Psychology; Medicine; Demographic economics; Business; Economics; Finance; Pension; Accounting; Economic growth","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006628498,0.0002403737,0.0007179035,0.0002144136,0.004806025,0.0002075285,0.0006323645,0.00008354385,0.000035943],"category_scores_gemma":[0.0001889612,0.0001564433,0.0002294823,0.0007204406,0.001124786,0.0001186903,0.00007430567,0.0002368722,0.000003357892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000903002,"about_ca_system_score_gemma":0.001275001,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6276059,"about_ca_topic_score_gemma":0.8973511,"domain_scores_codex":[0.9946216,0.001648269,0.001036391,0.0005749615,0.000950843,0.00116789],"domain_scores_gemma":[0.9964153,0.0007051759,0.001228634,0.0007371614,0.0001778512,0.000735932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001839195,0.0001173968,0.616456,0.0002978811,0.0003454561,1.955784e-7,0.3212247,0.00001351836,0.000001017978,0.05049136,0.006543292,0.004325243],"study_design_scores_gemma":[0.0003984183,0.001556513,0.9227044,0.0003440984,0.00005257638,4.686786e-8,0.04294595,0.00005953323,4.359456e-7,0.001086224,0.03067773,0.0001740626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485024,0.001625966,0.00000672034,0.04396394,0.0002470953,0.004115707,0.00008996308,0.00003201277,0.001416256],"genre_scores_gemma":[0.9926198,0.0006065291,0.00006476646,0.0022201,0.00007254083,0.0001769831,0.00003161309,0.00002548911,0.004182193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3062484,"threshold_uncertainty_score":0.9964896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2659343916369158,"score_gpt":0.4921154728935708,"score_spread":0.226181081256655,"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."}}