Purpose The current study was designed to investigate the differences in language input related to family factors (maternal level of education [MLE] and socioeconomic level of deprivation [SLD]) and their association with language outcomes in preschoolers. Method This study used New Zealand SLD and MLE classification systems to examine differences in language input related to these factors among 20 typically developing preschool children aged 2-5 years. The quantity of children's language input (adult words [AWs], conversational turns [CTs]) was calculated using the Language ENvironment Analysis audiotaping technology for two typical weekend days. Four 5-min Language ENvironment Analysis recording segments were transcribed and coded, and parental language strategies were classified as optimal language strategy, moderate language strategy, or sub-optimal language strategy (S-OLS) for child language outcomes. The receptive and expressive language of each child was assessed using the Preschool Language Scales-Fifth Edition. Results Mann-Whitney tests showed significant differences between the quantity of language input (AWs/hr, CTs/hr) for high and low MLE and high and low SLD groups. Consistent with the literature, the use of S-OLSs was significantly lower for families with high MLE ( = .25, IQR = .14) and low SLD ( = .22, IQR = .13) than for families with low MLE ( = .41, IQR = .24) and high SLD ( = .41, IQR = .26). Spearman correlation coefficients indicated significant associations between language input (AWs/hr, CTs/hr, S-OLSs) and language outcomes. Conclusions Reduced language input and the frequent use of S-OLSs associated with low maternal education and high deprivation and low language outcomes for these children highlight the importance for all parents/families to learn optimal language strategies to support the development of strong language skills in their children in young age.
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http://dx.doi.org/10.1044/2020_LSHSS-19-00095 | DOI Listing |
BMJ Qual Saf
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Guangzhou Cadre and Talent Health Management Center, Guangzhou, China.
Background: Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited.
Objective: We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2.
Alzheimers Dement
December 2024
Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan.
Background: Alzheimer's disease (AD) has been associated with speech and language impairment. Recent progress in the field has led to the development of automated AD detection using audio-based methods, because it has a great potential for cross-linguistic detection. In this investigation, we utilised a pretrained deep learning model to automatically detect AD, leveraging acoustic data derived from Chinese speech.
View Article and Find Full Text PDFFront Artif Intell
December 2024
Computer Science Department, Brandeis University, Waltham, MA, United States.
Multimodal dialogue involving multiple participants presents complex computational challenges, primarily due to the rich interplay of diverse communicative modalities including speech, gesture, action, and gaze. These modalities interact in complex ways that traditional dialogue systems often struggle to accurately track and interpret. To address these challenges, we extend the textual enrichment strategy of Dense Paraphrasing (DP), by translating each nonverbal modality into linguistic expressions.
View Article and Find Full Text PDFFront Psychiatry
December 2024
Department of Information Science, University of Regensburg, Regensburg, Germany.
Background: Up to 13% of adolescents suffer from depressive disorders. Despite the high psychological burden, adolescents rarely decide to contact child and adolescent psychiatric services. To provide a low-barrier alternative, our long-term goal is to develop a chatbot for early identification of depressive symptoms.
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