Purpose Home visiting programs have produced inconsistent outcomes. One challenge for the field is the design and implementation of effective training to support home visiting staff. In part due to a lack of formal training, most home visitors need to develop the majority of their skills on the job. Home visitors typically receive training in their agency's specific model (e.g., HFA, NFP) and, if applicable, curriculum. Increasingly, states and other home visiting systems are developing and/or coordinating more extensive training and support systems beyond model-specific and curricula trainings. To help guide these training efforts and future evaluations of them, this paper reviews research on effective training, particularly principles of training transfer and adult learning. Description Our review summarizes several meta-analyses, reviews, and more recent publications on training transfer and adult learning principles. Assessment Effective training involves not only the introduction and modeling of concepts and skills but also the practice of, evaluation of, and reflection upon these skills. Further, ongoing encouragement of, reward for, and reflection upon use of these skills, particularly by a home visitor's supervisor, are critical for the home visitor's continued use of these skills with families. Conclusion Application of principles of adult learning and training transfer to home visiting training will likely lead to greater transfer of skills from the training environment to work with families. The involvement of both home visitors and their supervisors in training is likely important for this transfer to occur.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10995-018-2554-6DOI Listing

Publication Analysis

Top Keywords

training transfer
16
training
15
effective training
12
adult learning
12
visiting training
8
guide training
8
training support
8
transfer adult
8
reflection skills
8
skills
6

Similar Publications

Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures.

View Article and Find Full Text PDF

One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.

View Article and Find Full Text PDF

Style Transfer of Chinese Wuhu Iron Paintings Using Hierarchical Visual Transformer.

Sensors (Basel)

December 2024

College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China.

Within the domain of traditional art, Chinese Wuhu Iron Painting distinguishes itself through its distinctive craftsmanship, aesthetic expressiveness, and choice of materials, presenting a formidable challenge in the arena of stylistic transformation. This paper introduces an innovative Hierarchical Visual Transformer (HVT) framework aimed at achieving effectiveness and precision in the style transfer of Wuhu Iron Paintings. The study begins with an in-depth analysis of the artistic style of Wuhu Iron Paintings, extracting key stylistic elements that meet technical requirements for style conversion.

View Article and Find Full Text PDF

A Combined CNN-LSTM Network for Ship Classification on SAR Images.

Sensors (Basel)

December 2024

ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.

Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.

View Article and Find Full Text PDF

Strategies to Enhance Diagnostic Capabilities for the New Drug-Resistant Tuberculosis (DR-TB) Drugs.

Pathogens

November 2024

Department of Pulmonary Diseases and Tuberculosis, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands.

The global burden of drug-resistant tuberculosis (DR-TB) continues to challenge healthcare systems worldwide. There is a critical need to tackle DR-TB by enhancing diagnostics and drug susceptibility testing (DST) capabilities, particularly for emerging DR-TB drugs. This endeavor is crucial to optimize the efficacy of new therapeutic regimens and prevent the resistance and overuse of these invaluable weapons.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!