{"id":"W2546525884","doi":"10.1177/2156869316671372","title":"Economic Conditions in Countries of Origin and Trajectories in Distress after Migration to Canada","year":2016,"lang":"en","type":"article","venue":"Society and Mental Health","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Immigration; Mental health; Demographic economics; Generalizability theory; Distress; Population; Metropolitan area; Country of origin; Demography; Psychology; Geography; Sociology; Political science; Economics; Developmental psychology; Psychiatry; Clinical psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001401444,0.00005920743,0.000147414,0.00001450917,0.0001856457,0.000001489974,0.00001590951,0.00002963349,0.00002835005],"category_scores_gemma":[0.000003880915,0.00004381121,0.00001058472,0.00003142676,0.00005916817,0.00005110691,0.00002405974,0.00005145247,7.418362e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004628777,"about_ca_system_score_gemma":0.0002975834,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3822964,"about_ca_topic_score_gemma":0.9820787,"domain_scores_codex":[0.9993929,0.00004931916,0.0002237123,0.0001040255,0.00004690874,0.0001831723],"domain_scores_gemma":[0.9997399,0.00009410075,0.00006251068,0.00003715356,0.000008717917,0.00005759226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003144304,0.00000840968,0.9778017,0.0001677898,0.000004827264,1.758406e-7,0.01318653,8.888146e-8,0.00001325772,0.001095908,0.007450197,0.000239657],"study_design_scores_gemma":[0.0007057242,0.00004489247,0.9654193,0.0003194772,0.000001257095,1.249786e-7,0.01649822,0.000001056359,0.00001267696,0.0001703745,0.01677022,0.00005669186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690735,0.0004416837,0.000001527146,0.0291835,0.00016412,0.0003432037,0.0007534656,0.000004119815,0.00003490918],"genre_scores_gemma":[0.9966821,0.00164383,0.00001165275,0.00129883,0.00002602414,0.00008308453,0.00001728791,0.000003533826,0.000233667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5997823,"threshold_uncertainty_score":0.6218169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630857351311333,"score_gpt":0.3576227916998916,"score_spread":0.3413142181867783,"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."}}