Exploratory Results From a Chatbot-Based Screening Tool for Computer-Mediated Communication Impairment.

Purpose: Computer-mediated communication (CMC) is a routine language modality, yet its disorder profile is poorly understood. Guided by exploratory frameworks, this study designed SpeechBot, a brief chatbot-based screener. It was piloted in an acute-care hospital to evaluate the tool's clinical feas...

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Publicado en:Perspectives of the ASHA Special Interest Groups Vol. 10; no. 6; pp. 1677 - 1697
Autor principal: Musaji, Imran
Formato: algorithm glossary research tables/charts Journal Article
Publicado: American Speech-Language-Hearing Association Dec2025
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Dec2025
      vid: 10
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      pub: American Speech-Language-Hearing Association
      place: Rockville, Maryland
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        10.1044/2025_PERSP-24-00064
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        atl: Exploratory Results From a Chatbot-Based Screening Tool for Computer-Mediated Communication Impairment.
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        au: Musaji, Imran
        affil: Department of Communication Sciences and Disorders, Wichita State University, KS
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        subj:
          Communicative Disorders Physiopathology
          Communication Evaluation
          Computer-Assisted Instruction
          Chatbot
          Health Screening
          Sign Language
          Human
          Male
          Female
          Adult
          Middle Age
          Pilot Studies
          Exploratory Research
          Comparative Studies
          Descriptive Statistics
          Data Analysis Software
          Spearman's Rank Correlation Coefficient
          Mann-Whitney U Test
          Nonparametric Statistics
          Sex Factors
          Hospitals
          Self Report
          Aged
          Conceptual Framework
          Convenience Sample
          Persons with Disabilities
          Task Performance and Analysis
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Male
          Female
      ab: Purpose: Computer-mediated communication (CMC) is a routine language modality, yet its disorder profile is poorly understood. Guided by exploratory frameworks, this study designed SpeechBot, a brief chatbot-based screener. It was piloted in an acute-care hospital to evaluate the tool's clinical feasibility and to probe for preliminary patterns of CMC impairment. Method: Thirty-three adults who reported regularly texting and could complete three inclusion items were screened during routine bedside speech and language evaluations. Speech-language pathologists summarized function across traditional language domains on 5-point Likert scales, while a CMC impairment score (CMCIS; 0--12) captured CMC function. Consistent with exploratory aims, no confirmatory hypotheses were tested. Results: Impairment scores clustered into three strata: no--minimal (CMCIS 0--3; 79%), moderate (4--6; 15%), and severe (≥ 7; 6%). Seven participants with moderate-to-severe CMCIS scores demonstrated no observed deficits in traditional speech-language domains during bedside evaluations. Higher CMCIS was associated with slower response times (ρ = .56, p = .001), but not with age or clinician-rated language scores. Conclusion: These exploratory findings support the feasibility of chatbot-based CMC screening and generate several lines of future inquiry, including refining CMC impairment metrics; conducting rigorous experimental research to contextualize CMC impairment against established cognitive--linguistic deficits; and validating CMC screening in larger, demographically diverse samples.
      pubtype: Academic Journal
      doctype:
        algorithm
        glossary
        research
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        Journal Article
      ougenre: Article
    language: English
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