Purpose: To understand the meanings that the therapeutic bond assumes for clinical speech therapists.
Methods: The research was approved by the Ethics Committee, being of a transversal character, with a quantitative-qualitative approach in the Content Analysis. The research with the participation of 96 clinical speech therapists, registered in the Speech Therapy Council of the 3rd region (CRFa 3), which covers the States of Paraná and Santa Catarina.
Results: Of the 96 speech therapists included, a significant part of the participants defined the therapeutic bond as a relationship/interaction. Regarding the role of the bond for the speech therapy clinical work, most professionals declared theirs as a fundamental basis and another part of the bond is necessary for the evolution/development of the patient.
Conclusion: It is possible to understand that, according to the therapeutic patients, it is essential to sustain, maintain the clinical work for users, impacting the resignification of the complaint and the minimization of the users' suffering.
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http://dx.doi.org/10.1590/2317-1782/20232022167pt | DOI Listing |
Cureus
December 2024
Research, Nibbot International, Mexico City, MEX.
Background: Autism spectrum disorder (ASD) is a heterogeneous neurobiological condition characterized by behavioral problems and delayed neurodevelopment. Although transcranial magnetic stimulation (TMS) has been proposed as an alternative treatment for patients with ASD because of its promising benefits in reducing repetitive behaviors and enhancing executive functions, the use of high-intensity pulses (Hi-TMS) appears to be related to the side effects of the therapy. Low-intensity TMS (Li-TMS) has been partially investigated, but it may have clinical effects on ASD and simultaneously increase treatment safety.
View Article and Find Full Text PDFFront Immunol
January 2025
Department of Neurology, Huashan Hospital, Fudan University and Institute of Neurology, Fudan University, Shanghai, China.
We report a case of optic neuritis (ON) secondary to autoimmune encephalitis (AE) in a patient with concomitant antibodies to N-methyl-D-aspartate receptor (NMDAR), gamma-aminobutyric acid-B receptor (GABAR), and myelin oligodendrocyte glycoprotein (MOG). The patient exhibited a constellation of symptoms, including vision loss, seizures, mental and behavioral disorders, cognitive impairment, and speech abnormalities. At the two-year follow-up, the patient's symptoms had abated entirely.
View Article and Find Full Text PDFAppetite
January 2025
Universit'e Sorbonne Paris Nord and Universit'e Paris Cit'e, Inserm, INRAE, CNAM, Center of Research in Epidemiology and StatisticS (CRESS), Nutritional Epidemiology, Research Team (EREN), F-93017, Bobigny, France.
BMJ Case Rep
January 2025
Maxillofacial Surgery, Waikato Hospital, Hamilton, New Zealand.
A man in his late 50s was referred by a speech and language therapist for consideration of a palatal lift prosthesis (PLP) to improve his speech intelligibility. He presented with hypokinetic dysarthria characterised by reduced loudness, breathy voice and hypernasality. The patient had a diagnosis of progressive muscular dystrophy and mobilised in a motorised wheelchair.
View Article and Find Full Text PDFDigit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
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