{"id":"W2159824972","doi":"10.1016/j.socscimed.2009.02.034","title":"Harms and benefits: Collecting ethnicity data in a clinical context","year":2009,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Migration, Health and Trauma","field":"Psychology","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; University of British Columbia","funders":"Michael Smith Health Research BC","keywords":"Ethnic group; Thematic analysis; Context (archaeology); Health care; Harm; Population; Health equity; Public relations; Medicine; Sociology; Qualitative research; Nursing; Political science; Psychology; Public health; Environmental health; Geography; Social psychology; Social science; Law","routes":{"ca_aff":true,"ca_fund":true,"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":[0.03613441,0.0004242352,0.001107105,0.002781274,0.00319922,0.00274586,0.00082716,0.00141755,0.001484795],"category_scores_gemma":[0.1198427,0.0005238676,0.001023885,0.00327865,0.001822058,0.003087008,0.00355708,0.002073846,0.0003935776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001520396,"about_ca_system_score_gemma":0.005690607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008017097,"about_ca_topic_score_gemma":0.02831992,"domain_scores_codex":[0.9515315,0.0358985,0.005414888,0.001410742,0.004509266,0.001235123],"domain_scores_gemma":[0.9189142,0.04612392,0.0145676,0.006693995,0.01050425,0.003196096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004381337,0.0006451259,0.9587293,0.0002195932,0.0002529137,0.0002280323,0.01018909,0.0002588282,0.0004588595,0.0009003318,0.00121855,0.0264613],"study_design_scores_gemma":[0.00009394175,0.000986125,0.9341788,0.0005726676,0.0005763027,0.00061478,0.04878727,0.001984466,0.001348437,0.004934849,0.005803679,0.000118717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880806,0.0006839678,0.003581709,0.002021754,0.0001095004,0.000531278,0.0005853231,0.00001266198,0.004393184],"genre_scores_gemma":[0.9901198,0.0004572919,0.006623246,0.0007082084,0.00008740417,0.001099,0.0004306073,0.00002307235,0.0004513937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03613441,"threshold_uncertainty_score":0.1910993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2166238656294923,"score_gpt":0.5053668026249081,"score_spread":0.2887429369954158,"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."}}