Understanding Players' Sportspersonship Attitude, Expectancy-Related Beliefs, and Subjective Task Values in Field Hockey: An Integrated Approach.

Int J Environ Res Public Health

Exercise and Sports Science Programme, School of Health Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia.

Published: April 2022

(1) Background: The purpose of this study was to examine the relationship between expectancy-value components and attitudes toward sportspersonship among Malaysian adolescent field hockey players. This study also examined the effect of expectancy beliefs, task values, and sportspersonship attitude on the motivation of adolescent field hockey players by gender and age group. (2) Methods: The Malay versioned Expectancy Value Model Questionnaire and the Malay versioned Multidimensional Sportspersonship Orientations Scale were administered on 730 respondents (µ = 15.46 ± 1.83 years). (3) Results: The expectancy values and attainment value (r = 0.894), utility value and attainment value (r = 0.833) were highly correlated. There was no significant gender difference in expectancy, task values, and sportspersonship attitude dimensions. The main effect of age group was significant on task values: (2724) = 4.19; = 0.01. The difference was indicated between age groups of 15-16 years and 12-14 years ( = 0.02, = 0.014) under task values variable. (4) Conclusions: There is no significant relationships between sportspersonship attitude (MSOS-M) and of expectancy beliefs and task values (EVMQ-M). To conclude, female and younger players demonstrate lower expectancy beliefs, task values, and sportspersonship attitudes than male and older field hockey players.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028397PMC
http://dx.doi.org/10.3390/ijerph19084819DOI Listing

Publication Analysis

Top Keywords

task values
28
sportspersonship attitude
16
field hockey
16
hockey players
12
expectancy beliefs
12
beliefs task
12
values sportspersonship
12
values
8
adolescent field
8
age group
8

Similar Publications

Purpose: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution -MRSI to accurately remove lipid and water signals while preserving the metabolite signal.

View Article and Find Full Text PDF

The diffusion generative model has achieved remarkable performance across various research fields. In this study, we propose a transferable graph attention diffusion model, GADIFF, for a molecular conformation generation task. With adopting multiple equivariant networks in the Markov chain, GADIFF adds GIN (Graph Isomorphism Network) to acquire local information of subgraphs with different edge types (atomic bonds, bond angle interactions, torsion angle interactions, long-range interactions) and applies MSA (Multi-head Self-attention) as noise attention mechanism to capture global molecular information, which improves the representative of features.

View Article and Find Full Text PDF

Introduction The degree to which each human brain hemisphere governs specific cognitive processes, such as language and handedness (the preference or dominance of one hand over the other), varies across individuals. Research has explored the nature of language laterality in left-handed (LH) individuals, indicating that left-hemisphere dominance for language is commonly observed across both left- and right-handed populations. Advanced imaging techniques, including functional transcranial Doppler sonography and fMRI, have revealed subtle differences in language lateralization between LH and right-handed (RH) individuals, particularly in semantic processing tasks.

View Article and Find Full Text PDF

Purpose: This study aimed to investigate the impact of different model retraining schemes and data partitioning on model performance in the task of COVID-19 classification on standard chest radiographs (CXRs), in the context of model generalizability.

Approach: Two datasets from the same institution were used: Set A (9860 patients, collected from 02/20/2020 to 02/03/2021) and Set B (5893 patients, collected from 03/15/2020 to 01/01/2022). An original deep learning (DL) model trained and tested in the task of COVID-19 classification using the initial partition of Set A achieved an area under the curve (AUC) value of 0.

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!