Two studies were conducted with distinct samples to investigate how motivational beliefs cohere and function together (i.e., motivational profiles) and predict academic adjustment. Integrating across motivational theories, participants ( = 160 upper elementary students; = 325 college students) reported on multiple types of motivation (achievement goals, task value, perceived competence) for schooling more generally (Study 1) and in science (Study 2). Three profiles characterized by and motivation were identified in both studies. Profiles characterized by motivation (Study 1) and (Study 2) were also present. Across studies, the and profiles were associated with the highest academic engagement and achievement. Findings highlight the benefit of integrating across motivational theories when creating motivational profiles, provide initial evidence regarding similarities and differences in integrative motivational profiles across distinct samples, and identify which motivational combinations are associated with beneficial academic outcomes in two educational contexts.
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http://dx.doi.org/10.1037/edu0000245 | DOI Listing |
Cell Syst
January 2025
Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA; Department of Pathology, University of Chicago, Chicago, IL 60637, USA; Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL 60637, USA. Electronic address:
The human gut microbiome contains many bacterial strains of the same species ("strain-level variants") that shape microbiome function. The tremendous scale and molecular resolution at which microbial communities are being interrogated motivates addressing how to describe strain-level variants. We introduce the "Spectral Tree"-an inferred tree of relatedness built from patterns of co-evolutionary constraint between greater than 7,000 diverse bacteria.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States.
Motivation: Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using this type of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type.
View Article and Find Full Text PDFBackground And Aims: The widespread popularity of video games reflects their appeal to meet fundamental needs. This study aims to investigate the psychological factors of gaming use, identifying profiles ranging from healthy to gaming disorder.
Methods: In this cross-sectional study, 5,222 participants were surveyed.
Health Expect
February 2025
Faculty of Communication, Culture and Society, Università della Svizzera italiana, Lugano, Switzerland.
Objectives: Grounded in the Health Empowerment Model, which posits that health literacy and patient empowerment are intertwined yet distinct constructs, this study investigates how the interplay of these factors influences attitudes toward seeking professional psychological help in members of online communities for mental health (OCMHs). This while acknowledging the multidimensionality of patient empowerment, encompassing meaningfulness, competence, self-determination, and impact.
Design And Methods: A cluster analysis of data gathered from 269 members of Italian-speaking OCMHs on Facebook has been performed.
Brief Bioinform
November 2024
GENYO, Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS Granada, Avenida de la Ilustración 114, Granada 18016, Spain.
Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications.
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