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POSTER ABSTRACT:
The following is the abstract submitted to ASHA and also the full list of references we used for this study, not just those used for the poster.
Authors: Jennifer Mozeiko, Phd, CCC-SLP; Katherine Konishesky, MA, CCC-SLP
Title: Simplifying Discourse Analysis for Clinical Use.
Learning objective: Discourse analysis provides one way to identify the subtle impairments that may characterize the language of people with mild aphasia. These are valuable but time consuming. “Real time” verb counts provide a potential solution to this problem.
Biosketch: Jennifer Mozeiko, PhD, CCC-SLP, is an Assistant Professor in the Department of Speech, Language, and Hearing Sciences at the University of Connecticut. Her research is focused on determining optimal dosage parameters (session length, session frequency, total treatment duration, etc.) for people with chronic aphasia. She is also interested in improving functional treatment options for people with mild aphasia, with an emphasis on return to employment.
Short Abstract
Discourse analysis is an alternative to insufficient assessments for people with mild aphasia. By calculating correct informational units, one can determine both the efficiency and the informativeness of discourse. While effective, this method is time consuming and unreasonable for a busy SLP. Using 329 transcripts from AphasiaBank, we explore whether simple verb usage is predictive of each these discourse measures.
Extended Abstract
Introduction
Moderate and severe aphasia deficits tend to be the focus of most treatment studies and standardized assessment batteries are designed to detect these types of impairments (Ross & Wertz, 2004). Though people with mild aphasia (PWMA) may be able to hold a conversation, or tell a narrative, they feel that their communicative abilities are not what they used to be and find they are “held back” by what they cannot say (Cruice, Worrall & Hickson, 2006) and may perceive their quality of life to be as disrupted as those with more severe deficits (Williamson, 2011). Standardized batteries lack sensitivity but discourse measures can demonstrate impairments warranting language treatment (Fromm et al., 2016). Unfortunately, these are time consuming and impractical for a busy speech-language pathologist (Bryant, Spencer, & Ferguson, 2017).
Aims
In order to identify a faster, simpler method of discourse analysis, we explore the relationship between verb use and informativeness, efficiency, and overall CIU count. Specifically, we investigate whether the number of:
- verbs used are associated with more informative discourse as measured by CIUs/word count
- verb errors are associated with better discourse efficiency as measured by CIUs/minutes
- verbs used are associated with more CIUs
Method
Language samples were obtained from AphasiaBank (MacWhinney, Fromm, Holland, 2011) from all participants classified as anomic (n=102) or not aphasic (n=27) on the Western Aphasia Battery (Kertesz, 2006) (see Table 1). The second author and two research assistants calculated total verbs used correctly and incorrectly and CIU counts (see Nicholas & Brookshire, 1993) for each transcript. Greater than 90% intra- and inter-reliability was established between research assistants. Strength of associations were determined based on
correlation coefficients. Simple linear regressions were conducted for variables with significant correlations to determine whether verb use predicted the various discourse measures.
Results
There was a significant weak correlation between informativeness and total number of verbs used (r=.016, n= 119, p= .000) (see Table 2 and Figure 1); a significant moderate correlation between efficiency and total number of verbs used (r=.457, n= 117, p=.000) (see Figure 2); and a strong significant relationship between the total number of CIUs and total number of verbs used (r= .811, n= 119, p= .000) (see Figure 3). Results of the regression indicated that correct verb use explained 20.7% of the variance in efficiency and total number of verbs explained 65.8% of the variance in total CIUs (see Table 2).
Conclusion
The simple tallying of verbs may offer an efficient, practical alternative to analyzing discourse for CIUs. CIU analysis requires transcription and evaluation of each word as it relates to the sample but counting verbs is something that can be done in “real time” making transcription unnecessary. Our results suggest that CIU counts can be predicted by total verb use and, by extension, the efficiency and informativeness of discourse. These results may have immediate clinical utility.
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