{"id":"W2068902053","doi":"10.1002/nur.20364","title":"Cultural adaptation and translation of measures: An integrated method","year":2010,"lang":"en","type":"article","venue":"Research in Nursing & Health","topic":"Nursing Diagnosis and Documentation","field":"Nursing","cited_by":204,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Toronto; University of British Columbia; Toronto Metropolitan University; Research Canada","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Ryerson University","keywords":"Operationalization; Conceptualization; Equivalence (formal languages); Adaptation (eye); Set (abstract data type); Conceptual framework; Translation (biology); Computer science; Process (computing); Dynamic and formal equivalence; Psychology; Natural language processing; Artificial intelligence; Sociology; Epistemology; Linguistics; Machine translation; Social science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1881352,0.002591131,0.00291893,0.0113496,0.003284085,0.005156716,0.003305473,0.001591,0.009107167],"category_scores_gemma":[0.2693858,0.002251885,0.002800811,0.01229817,0.003134782,0.004113168,0.009804857,0.00460047,0.004624261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002951674,"about_ca_system_score_gemma":0.01137933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680464,"about_ca_topic_score_gemma":0.003465944,"domain_scores_codex":[0.7931244,0.1564194,0.02403855,0.009762762,0.01567129,0.0009836325],"domain_scores_gemma":[0.7536191,0.1394792,0.01228359,0.04037916,0.05308808,0.001150766],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002876266,0.0005775957,0.003234692,0.002413613,0.0003664658,0.0001725762,0.0184429,0.0009513063,0.005631969,0.01027703,0.005020157,0.9526241],"study_design_scores_gemma":[0.004809412,0.006952384,0.08550648,0.01982453,0.00303353,0.00365286,0.05750511,0.07742057,0.06226847,0.1673805,0.5093303,0.002315841],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009104584,0.0004260343,0.9594608,0.0004217591,0.0004137798,0.02478288,0.0003151701,0.001184213,0.00389072],"genre_scores_gemma":[0.01082113,0.0002179892,0.9652635,0.0001039818,0.00004441988,0.02259091,0.0001832772,0.0002019442,0.0005728565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8118649,"threshold_uncertainty_score":0.9949658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1987719875858697,"score_gpt":0.5370768812550216,"score_spread":0.338304893669152,"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."}}