Background: A theoretical and empirical base for CBT training and supervision has started to emerge. Increasingly sophisticated maps of CBT therapist competencies have recently been developed, and there is evidence that CBT training and supervision can produce enhancement of CBT skills. However, the evidence base suggesting which specific training techniques are most effective for the development of CBT competencies is lacking.
Aims: This paper addresses the question: What training or supervision methods are perceived by experienced therapists to be most effective for training CBT competencies?
Method: 120 experienced CBT therapists rated which training or supervision methods in their experience had been most effective in enhancing different types of therapy-relevant knowledge or skills.
Results: In line with the main prediction, it was found that different training methods were perceived to be differentially effective. For instance, reading, lectures/talks and modelling were perceived to be most useful for the acquisition of declarative knowledge, while enactive learning strategies (role-play, self-experiential work), together with modelling and reflective practice, were perceived to be most effective in enhancing procedural skills. Self-experiential work and reflective practice were seen as particularly helpful in improving reflective capability and interpersonal skills.
Conclusions: The study provides a framework for thinking about the acquisition and refinement of therapist skills that may help trainers, supervisors and clinicians target their learning objectives with the most effective training strategies.
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http://dx.doi.org/10.1017/S1352465809990270 | DOI Listing |
ACS Sens
January 2025
Department of Engineering Physics, McMaster University, 1280 Main Street West, L8S 4L8 Hamilton, Ontario, Canada.
Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.
View Article and Find Full Text PDFHum Genet
January 2025
TCS Research, Tata Consultancy Services, Hyderabad, India.
Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease, thus posing a challenge in the interpretation of genetic testing results. Here we report an improved method for predicting the VUS of Arylsulfatase A (ARSA) gene as part of the Critical Assessment of Genome Interpretation challenge (CAGI6). Our method uses a transfer learning approach that leverages a pre-trained protein language model to predict the impact of mutations on the activity of the ARSA enzyme, whose deficiency is known to cause a rare genetic disorder, metachromatic leukodystrophy.
View Article and Find Full Text PDFProbl Endokrinol (Mosk)
January 2024
Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.
View Article and Find Full Text PDFFront Immunol
January 2025
School of Nursing, Zunyi Medical University, Zunyi, China.
Background: Most patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients.
View Article and Find Full Text PDFJ Geriatr Emerg Med
December 2024
Geriatric Research Education and Clinic Center, James J. Peters VA Medical Center, 130 W Kingsbridge Rd, Bronx, NY 10468 & Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029.
Background: Older adults treated in emergency departments (EDs) are at higher risk for adverse outcomes. Using multiple facilities can worsen this issue through service duplication and poor care transitions. Veterans with dual insurance coverage can access both Veterans Health Administration (VHA) and non-VHA EDs.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!