Midrange Theory Evaluation to Advance Nursing Knowledge.

Nurs Sci Q

College of Nursing, The University of Arizona, Tucson, AZ, USA.

Published: July 2022

The author in this article presents a midrange theory evaluation framework as an update to nursing publications over the past 50 decades on theory evaluation criteria and incorporates recent philosophical perspectives on scientific theory and knowledge development. The intent also is to encourage a theorizing style that advances understanding and explanations of nursing phenomena for nursing practice as well as for the pure joy of knowing why something happens.

Download full-text PDF

Source
http://dx.doi.org/10.1177/08943184221092426DOI Listing

Publication Analysis

Top Keywords

theory evaluation
12
midrange theory
8
evaluation advance
4
nursing
4
advance nursing
4
nursing knowledge
4
knowledge author
4
author article
4
article presents
4
presents midrange
4

Similar Publications

Background: The global aging population and rapid development of digital technology have made health management among older adults an urgent public health issue. The complexity of online health information often leads to psychological challenges, such as cyberchondria, exacerbating health information avoidance behaviors. These behaviors hinder effective health management; yet, little research examines their mechanisms or intervention strategies.

View Article and Find Full Text PDF

The objective of this study was to develop and to test the validity and reliability of a survey aimed to evaluate internal and external factors associated with college food insecurity. Researchers used a mixed methods approach to evaluate the College Perspectives around Food Insecurity survey. Survey items were constructed from interview data and assigned a social cognitive theory concept (environment, personal, or behavior).

View Article and Find Full Text PDF

Identity as a resource or a demand.

PLoS One

January 2025

Department of Psychology, University of Rochester, Rochester, New York, United States of America.

Individuals embody various social identities that can impact how they interface with the social environment. Stigma theories suggest that members of low-status or marginalized groups possess devalued social identities, and therefore, experience more stress. While social identities can lead to increased stress, individuals' appraisals of their identities are not necessarily perceived as harmful/demanding.

View Article and Find Full Text PDF

QUEST#4X: An Extension of QUEST#4 for Benchmarking Multireference Wave Function Methods.

J Chem Theory Comput

January 2025

Qingdao Institute for Theoretical and Computational Sciences and Center for Optics Research and Engineering, Shandong University, Qingdao 266237, China.

Given a number of data sets for evaluating the performance of single reference methods for the low-lying excited states of closed-shell molecules, a comprehensive data set for assessing the performance of multireference methods for the low-lying excited states of open-shell systems is still lacking. For this reason, we propose an extension (QUEST#4X) of the radical subset of QUEST#4 ( , , 3720) to cover 110 doublet and 39 quartet excited states. Near-exact results obtained by iterative configuration interaction with selection and second-order perturbation correction (iCIPT2) are taken as benchmark to calibrate static-dynamic-static configuration interaction (SDSCI) and static-dynamic-static second-order perturbation theory (SDSPT2), which are minimal MRCI and CI-like perturbation theory, respectively.

View Article and Find Full Text PDF

Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.

Adv Sci (Weinh)

January 2025

Department of Molecular Pharmacology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China.

Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases.

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!