Objective: This multimethod prospective study examined whether emotional disclosure and coping focus as conveyed through natural language use are associated with the psychological and marital adjustment of head and neck cancer patients and their spouses.
Method: One-hundred twenty-three patients (85% men; age X¯ = 56.8 years, SD = 10.4) and their spouses completed surveys prior to, following, and 4 months after engaging in a videotaped discussion about cancer in the laboratory. Linguistic inquiry and word count (LIWC) software assessed counts of positive/negative emotion words and first-person singular (I-talk), second person (you-talk), and first-person plural (we-talk) pronouns. Using a grounded theory approach, discussions were also analyzed to describe how emotion words and pronouns were used and what was being discussed.
Results: Emotion words were most often used to disclose thoughts/feelings or uncertainty about the future, and to express gratitude or acknowledgment to one's partner. Although patients who disclosed more negative emotion during the discussion reported more positive mood following the discussion (p < .05), no significant associations between emotion word use and patient or spouse psychological and marital adjustment were found. Patients used significantly more I-talk than spouses and spouses used significantly more you-talk than patients (ps < .01). Patients and spouses reported more positive mood following the discussion when they used more we-talk. They also reported less distress at the 4-month follow-up when their partners used more we-talk during the discussion (p < .01).
Conclusion: Findings suggest that emotional disclosure may be less important to one's cancer adjustment than having a partner who one sees as instrumental to the coping process. (PsycINFO Database Record
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http://dx.doi.org/10.1037/hea0000377 | DOI Listing |
Sci Rep
December 2024
Department of Dermatology, Niazi Hospital, Lahore, Pakistan.
With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information.
View Article and Find Full Text PDFNat Commun
December 2024
LEADS group, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands.
Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional and transparent structures are typically easier to learn than those with opaque and irregular structures.
View Article and Find Full Text PDFEcol Lett
January 2025
Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland.
In the fields of ecology and conservation, taxonomic and geographic biases may compromise scientific progress. Using pollinator research as a case study, we evaluate four drivers of these biases and propose solutions to address (i) untested generalisations from highly studied taxa, (ii) information accessibility, (iii) scattered environmental regulations and (iv) restricted infrastructure and funding resources. Expanding the taxonomic, functional and geographic breadth of research and legislation, and involving scientists in policymaking, can generate greater equity, accessibility and impact of future science.
View Article and Find Full Text PDFFront Comput Neurosci
December 2024
School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
This study aims to enhance the classification accuracy of adverse events associated with the da Vinci surgical robot through advanced natural language processing techniques, thereby ensuring medical device safety and protecting patient health. Addressing the issues of incomplete and inconsistent adverse event records, we employed a deep learning model that combines BERT and BiLSTM to predict whether adverse event reports resulted in patient harm. We developed the Bert-BiLSTM-Att_dropout model specifically for text classification tasks with small datasets, optimizing the model's generalization ability and key information capture through the integration of dropout and attention mechanisms.
View Article and Find Full Text PDFCureus
November 2024
Urogynecology, Yokosuka Urogynecology and Urology Clinic, Yokosuka, JPN.
Background Ninjin'yoeito (NYT), a traditional Japanese Kampo medicine, has shown potential in treating frailty and overactive bladder (OAB) symptoms. However, its effects are multifaceted and vary among individuals. This pilot study explored the use of topological data analysis (TDA) and natural language processing (NLP) to evaluate the effect of NYT on frailty in patients with OAB.
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