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SADL量表在私人诊所中的临床应用II:适配变量的预测效度。日常生活中对放大的满意度。

Clinical application of the SADL scale in private practice II: predictive validity of fitting variables. Satisfaction with Amplification in Daily Life.

作者信息

Hosford-Dunn H, Halpern J

机构信息

Tai Inc., Tucson, Arizona 85751, USA.

出版信息

J Am Acad Audiol. 2001 Jan;12(1):15-36.

PMID:11214976
Abstract

Predictive validity of 44 independent variables and their interactions with Satisfaction with Amplification in Daily Life (SADL) scores was assessed. SADL scores were influenced by patient age, years of hearing aid experience, hours of use per day, perceived hearing difficulty, pure-tone average, hearing aid style, processor type, and manufacturer's invoice cost. The relative importance of these variables to SADL measures was complex and very small, but the variables and their squares and interactions improved r2 predictions of SADL Global and subscale scores in a separate stepwise multiple linear regression procedure by 12 to 33 percent compared to SADL norms alone. More research with additional variables is needed to develop a clinically useful model for predicting wearer satisfaction. Clinically, SADL scores yield subscale-specific patterns of satisfaction and dissatisfaction that help in intervention planning and serve as graphic "snapshots" of satisfaction status. A series of patient profiles are presented illustrating the potential usefulness of the SADL in predicting hearing aid satisfaction. With its good construct and psychometric properties, the SADL could serve as a gold standard for satisfaction outcomes and a basis for development of a predictive model of hearing aid fitting success.

摘要

评估了44个自变量及其与日常生活放大满意度(SADL)得分之间的交互作用的预测效度。SADL得分受患者年龄、助听器使用年限、每日使用时长、感知听力困难程度、纯音平均值、助听器款式、处理器类型以及制造商发票成本的影响。这些变量对SADL测量的相对重要性较为复杂且非常小,但在单独的逐步多元线性回归过程中,与仅使用SADL规范相比,这些变量及其平方和交互作用使SADL总体得分和子量表得分的r2预测提高了12%至33%。需要开展更多涉及其他变量的研究,以开发出临床上有用的预测佩戴者满意度的模型。临床上,SADL得分产生特定子量表的满意和不满意模式,有助于干预计划的制定,并作为满意度状况的直观“快照”。呈现了一系列患者概况,说明了SADL在预测助听器满意度方面的潜在用途。凭借其良好的结构和心理测量特性,SADL可作为满意度结果的金标准以及开发助听器适配成功预测模型的基础。

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