Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
This experiment used a pyramidal training model to evaluate the effects of behavioral skills training (BST), delivered in a 1-time group-training format, on the extent to which 25 human service staff implemented BST when training others how to implement behavioral procedures. Results indicated that (a) the training workshop increased BST integrity to mastery levels for the majority of participants with varying levels of education, organizational positions, and training experience, (b) the training effects generalized to teaching an untrained skill, and (c) high levels of BST integrity maintained at follow-up 4 to 6 weeks after training for all 3 participants with whom probes were conducted. Moreover, participants indicated high levels of satisfaction with both the training workshop and BST as a training procedure.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/jaba.608 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Analyzing the toxicological effects of PET-MPs on male infertility: Insights from network toxicology, mendelian randomization, and transcriptomics.
Reprod Biol
September 2025
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, No 218 Jixi Road, Hefei Anhui230022, China; Key Laboratory of Population Health Across
Current research indicates that polyethylene terephthalate microplastics (PET-MPs) may significantly impair male reproductive function. This study aimed to investigate the potential molecular mechanisms underlying this impairment. Potential gene targets of PET-MPs were predicted via the SwissTargetPrediction database.
View Article and Find Full Text PDFPerformance evaluation of a p-type co-axial HPGe detector: Long-term analysis of key parameters for gamma spectroscopy.
Appl Radiat Isot
September 2025
Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
Accurate determination of the parameters of each high purity germanium, HPGe detectors ensure the precision of quantitative results obtained from spectrum analysis. This study presents a comprehensive performance evaluation and long-term quality control assessment of a high-purity germanium (HPGe) gamma spectrometry system that has been operational for over 15 years. Key spectrometric measures were recorded, including energy resolution, peak shape ratios, asymmetry, peak-to-Compton ratio, relative efficiency, electronic noise, minimum detectable activity (MDA), and repeatability and reproducibility of the system.
View Article and Find Full Text PDFA novel multimodal framework combining habitat radiomics, deep learning, and conventional radiomics for predicting MGMT gene promoter methylation in Glioma: Superior performance of integrated models.
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
Designing Buchwald-Hartwig Reaction Graph for Yield Prediction.
J Org Chem
September 2025
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B-H reaction data set, B-H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an score of 0.971 while maintaining low computational costs.
View Article and Find Full Text PDF