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Despite the fairly widespread adoption of the 10-item Purdue Pharmacist Directive Guidance Scale (PPDG) over the last decade, only one study has assessed its psychometric properties. The present study examined the validity and reliability of the scale in a sample of 99 diabetic patients in the Southwestern United States. Principal axis factor analysis with Varimax rotation yielded two factors, "Instruction" and "Feedback and Goal Setting," similar to those found when the scale was originally developed. Cronbach's alphas for the total scale and the two factors were 0.95, 0.95, and 0.92, respectively. The scale and the two factors correlated significantly and positively with number of visits by patients to the pharmacist in the past 3 mo. These results provide further evidence for the internal consistency, and construct and criterion-related validities of the scale. 相似文献
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Meaning depends on context. This applies in obvious cases like deictics or sarcasm as well as more subtle situations like framing or persuasion. One key aspect of this is the identity of the participants in an interaction. Our interpretation of an utterance shifts based on a variety of factors, including personal history, background knowledge, and our relationship to the source. While obviously an incomplete model of individual differences, demographic factors provide a useful starting point and allow us to capture some of this variance. However, the relevance of specific demographic factors varies between situations—where age might be the key factor in one context, ideology might dominate in another. To address this challenge, we introduce a method for combining demographics and context into situated demographic embeddings—mapping representations into a continuous geometric space appropriate for the given domain, showing the resulting representations to be functional and interpretable. We further demonstrate how to make use of related external data so as to apply this approach in low‐resource situations. Finally, we show how these representations can be incorporated into improve modeling of real‐world natural language understanding tasks, improving model performance and helping with issues of data sparsity. 相似文献
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