Background: In accordance with Knowles's theory, self-directed learning (SDL) may be improved with tutorial strategies focused on guided reflection and critical analysis of the learning process. No evidence on effects on SDL abilities of different tutorial strategies offered to nursing students during the 1st clinical experience is available.
Objectives: To evaluate the effect of different tutorial strategies offered to nursing students on their SDL abilities.
Design: A pre-post intervention non-equivalent control group design was adopted in 2013. For the treatment group, structured and intensive tutorial interventions including different strategies such as briefing, debriefing, peer support, Socratic questioning, performed by university tutors were offered during the 1st clinical experience; for the control group, unstructured and non-intensive tutorial strategies were instead offered.
Setting: Two Bachelor of Nursing Degree.
Participants: Students awaiting their clinical experience (n=238) were the target sample. Those students who have completed the pre- and the post-intervention evaluation (201; 84.4%) were included in the analysis.
Methods: SDL abilities were measured with the SRSSDL_ITA (Self Rating Scale of Self Directed Learning-Italian Version). A multiple linear regression analysis was developed to explore the predictive effect of individual, contextual and intervention variables.
Results: Three main factors explained the 36.8% of the adjusted variance in SDL scores have emerged: a) having received a lower clinical nurse-to-student supervision (B 9.086, β 2.874), b) having received higher level and structured tutorial intervention by university tutors (B 8.011, β 2.741), and c) having reported higher SDL scores at the baseline (B .550, β .556).
Conclusions: A lower clinical nurse-to-student ratio (1:4), accompanied by unstructured and non-intensive tutorial intervention adopted by university tutors, seemed to be equivalent to an intensive clinical supervision (1:1) accompanied by higher level and structured tutorial strategies activated by the university tutors.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.nedt.2015.02.004 | DOI Listing |
Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, matching patients to clinical trials, and answering medical questions. In this primer paper, we propose an actionable guideline to help healthcare professionals more efficiently utilize LLMs in their work, along with a set of best practices.
View Article and Find Full Text PDFMed Ref Serv Q
January 2025
Medical Center Library, University of Kentucky Libraries, Lexington, KY, USA.
This paper describes a web-based resource that aims to improve health disparities research by providing guidance and tools for searching and evaluating information on vulnerable populations. The resource integrates electronic books on equity, diversity, and inclusion with interactive tutorials and modules teaching users to formulate research questions, select appropriate search terms, and appraise their searches. The resource also addresses the issue of biased and outdated searching terminology and offers alternative strategies for finding literature.
View Article and Find Full Text PDFBehav Anal Pract
December 2024
Simmons University, Boston, MA USA.
Unlabelled: One of the most critical intervention strategies when working with individuals with significant language delays associated with autism spectrum disorder and related developmental delays is teaching mands. For mand training to be effective, an establishing operation (EO) must be in effect, yet EOs are often difficult to observe. Before learning to mand, an individual may point to or approach a reinforcer, which likely indicates an EO related to that reinforcer, and may be considered an indicating response (IR).
View Article and Find Full Text PDFChemistry
January 2025
Division of Molecular Imaging and Photonics, Department of Chemistry, Katholieke Universiteit Leuven, Celestijnenlaan 200F, 3001, Leuven, Belgium.
Fluorescence spectroscopy and related techniques benefit from exceptional sensitivity and have become engrained in a variety of fields from biosciences to materials sciences. Measuring time-domain fluorescence decays is nowadays a routine task in many laboratories across these different fields. Perhaps surprisingly, a correct data analysis of these fluorescence decay curves presents a formidable challenge and requires extensive insight in the problems associated with fitting this type of data.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA.
The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and as a predictive tool for many optimization objectives. This review is aimed as a tutorial for those entering the area of statistical modeling in chemistry. We provide case studies to highlight the considerations and approaches that can be used to successfully analyze datasets in low data regimes, a common situation encountered given the experimental demands of organic chemistry.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!