Effects of Semantic Feature Type, Diversity, and Quantity on Semantic Feature Analysis Treatment Outcomes in Aphasia
Effects of Semantic Feature Type, Diversity, and Quantity on Semantic Feature Analysis Treatment Outcomes in Aphasia
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DOI：
10.1044/2020_ajslp-19-00112
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发表时间：
2021-02-01
影响因子：
2.6
通讯作者：
Dickey, Michael Walsh
中科院分区：
文献类型：
--
作者：
Evans, William S.;Cavanaugh, Rob;Dickey, Michael Walsh
目的：语义特征分析 (SFA) 是一种命名治疗方法，可提高失语症患者已处理和语义相关的未处理单词的命名性能。一个关键的治疗组成部分是要求患者生成治疗项目的语义特征。本文以多种方式研究了特征生成在 SFA 治疗反应中的作用：它试图复制 Gravier 等人的初步发现。 (2018)，发现特征生成可以预测经过训练和未经训练的单词的治疗相关增益。它检查了特征多样性或特定类别中生成的特征数量是否对 SFA 治疗结果产生差异影响。方法：44 名患有慢性失语症的参与者每天接受 SFA 治疗，持续 4 周。在多基线设计中按顺序对多个列表进行治疗。在治疗期间捕获参与者生成的特征，并根据特征类别、每次试验生成的平均特征总数以及每个项目生成的独特特征总数进行编码。使用逻辑混合效应回归模型分析项目级命名准确性。结果：与 Gravier 等人相比，我们发现生成更多参与者生成的特征可以改善 SFA 中经过训练但未经训练的项目的治疗反应。 （2018）。参与者生成的特征多样性或特征类别对 SFA 治疗结果没有任何差异影响。结论：患者生成的特征仍然是 SFA 中直接训练效果和总体治疗反应的关键预测因素。失语症的严重程度也是治疗结果的重要预测因素。未来的工作应侧重于识别潜在的治疗无反应者，并探索治疗修改以改善这些人的治疗结果。
Purpose: Semantic feature analysis (SFA) is a naming treatment found to improve naming performance for both treated and semantically related untreated words in aphasia. A crucial treatment component is the requirement that patients generate semantic features of treated items. This article examined the role feature generation plays in treatment response to SFA in several ways: It attempted to replicate preliminary findings from Gravier et al. (2018), which found feature generation predicted treatment-related gains for both trained and untrained words. It examined whether feature diversity or the number of features generated in specific categories differentially affected SFA treatment outcomes.Method: SFA was administered to 44 participants with chronic aphasia daily for 4 weeks. Treatment was administered to multiple lists sequentially in a multiple-baseline design. Participant-generated features were captured during treatment and coded in terms of feature category, total average number of features generated per trial, and total number of unique features generated per item. Item-level naming accuracy was analyzed using logistic mixed-effects regression models.Results: Producing more participant-generated features was found to improve treatment response for trained but not untrained items in SFA, in contrast to Gravier et al. (2018). There was no effect of participant-generated feature diversity or any differential effect of feature category on SFA treatment outcomes.Conclusions: Patient-generated features remain a key predictor of direct training effects and overall treatment response in SFA. Aphasia severity was also a significant predictor of treatment outcomes. Future work should focus on identifying potential nonresponders to therapy and explore treatment modifications to improve treatment outcomes for these individuals.