Cross-cultural adaptation of the Skin Cancer Index into Brazilian Portuguese for patients with cervicofacial nonmelanoma skin cancer

Supportive Care in Cancer(2023)

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摘要
Purpose This study aimed to translate the Skin Cancer Index (SCI) into Portuguese, adapt it for Brazilian culture, and clinically validate it. Methods A five-stage cross-cultural adaptation model was followed, with subsequent clinical validation. Inter-rater agreement was assessed using the content validity index (CVI). The hypothesis of the non-inferiority of the CVI at 80% probability level was evaluated using an exact binomial test. We used Spearman’s rank-order and Pearson’s product–moment correlation analysis, internal consistency using McDonald’s ω and Cronbach’s α metric, and construct validity using confirmatory factor analysis. The factorial model was validated using the chi-squared test, root mean square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean square residual (SRMR). Results The first stage yielded two independent translations. After synthesis, back-translation, and review, the prefinal version was tested on 40 patients. Inter-rater agreement indices on content validity were significantly higher than 80% ( p < 0.05). The SCI remained stable, and the Spearman’s rank-order ( r s ), Pearson product–moment ( r ), and intraclass correlation coefficients were > 0.9, indicating excellent reliability. The reliability of McDonald’s ω was considered ideal (> 0.8) in all subdimensions and scale. Cronbach’s α was considered ideal in the “Emotional” and “Social” subdimensions and scale. Construct validity was observed in all subdimensions and scale through the criteria ( χ 2 ) p value > 0.05, RMSEA < 0.08, CFI ≥ 0.9, and SRMR ≤ 0.08. Conclusion The cross-cultural adaptation of the SCI to Portuguese for Brazilian culture showed content validity and reliability, contributing to quality of life assessment in patients with NMSC.
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关键词
Cross-cultural adaptation model,Nonmelanoma skin cancer,Portuguese,Skin Cancer Index
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