{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004522935,0.0001102969,0.000228144,0.0004966857,0.000244811,0.00009136005,0.0001028657,0.0001143498,0.00001089203],"category_scores_gemma":[0.0001967038,0.0001027599,0.00002626389,0.0007036795,0.000230971,0.0005276386,0.000003870859,0.0006892226,0.000001602123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002754015,"about_ca_system_score_gemma":0.0001486493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003856918,"about_ca_topic_score_gemma":0.001848179,"domain_scores_codex":[0.9968389,0.001367547,0.0004158821,0.0003240064,0.0006062624,0.0004474377],"domain_scores_gemma":[0.9988301,0.0004142234,0.0001081158,0.0001911243,0.0002931212,0.0001633298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003377808,0.0003001434,0.001242207,0.00007702502,0.000002267251,4.115567e-7,0.01990545,0.00008265931,0.01912294,0.001498168,0.0002724831,0.9571584],"study_design_scores_gemma":[0.01300987,0.01388414,0.3209822,0.01247119,0.00007982571,0.0001227035,0.1071526,0.3141998,0.07231542,0.1290931,0.01512447,0.001564725],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719093,0.001029184,0.006078047,0.01831796,0.001050559,0.0009917564,0.00001200067,0.0000688199,0.0005424152],"genre_scores_gemma":[0.9477313,0.00007769719,0.05193687,0.00006479576,0.00008812051,0.00001763797,0.00005872122,0.00001986269,0.000004976167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9555937,"threshold_uncertainty_score":0.5830532,"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."}}