Background: Aspects of the learning environment may be related to students` approaches to studying, but few studies have investigated these relationships in the context of occupational therapy education.
Objective: To examine associations between occupational therapy students' perceptions of the learning environment and their approaches to studying.
Method: One hundred eighty-seven first-year occupational therapy students in Norway (response rate 61.3%) participated in this study. Aside from sociodemographic information, the students completed the Course Experience Questionnaire and the Approaches and Study Skills Inventory for Students. Associations between learning environment variables and study approaches were investigated with hierarchical linear regression analyses.
Results: Higher scores on Generic skills were associated with higher scores on the deep and strategic approach scales (β ranging 0.18-0.51), while lower scores were associated with higher surface approach scale scores (β = - 0.24). Lower scores on Clear goals and standards and Appropriate workload were associated with higher surface approach scores (β ranging - 0.16 - -0.42).
Conclusion: By improving aspects of the learning environment, there may be a potential for influencing occupational therapy students' approaches to studying. Based on this study, emphasizing how generic skills developed in the study program may become useful in practising a profession, ensuring clarity of goals and standards, and maintaining an appropriate workload on students appear to be important.
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http://dx.doi.org/10.1186/s12909-020-02033-4 | DOI Listing |
Nat Neurosci
January 2025
Brain Research Institute, University of Zurich, Zurich, Switzerland.
Appropriate risk evaluation is essential for survival in complex, uncertain environments. Confronted with choosing between certain (safe) and uncertain (risky) options, animals show strong preference for either option consistently across extended time periods. How such risk preference is encoded in the brain remains elusive.
View Article and Find Full Text PDFPlant Genome
March 2025
USDA-ARS Southeast Area, Plant Science Research, Raleigh, North Carolina, USA.
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions.
View Article and Find Full Text PDFEnviron Res
January 2025
School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, China; Pearl River Estuary Marine Ecosystem Research Station, Ministry of Education, Zhuhai, 519082, China; Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Zhuhai, 519082, China.
Temporal variability and associated driving factors of sea surface chlorophyll-a concentration (Chl-a) in coastal waters have been extensively studied worldwide; however, the importance and spatial heterogeneity of these driving factors remain insufficiently documented. This study addressed this gap by investigating the Pearl River Estuary (PRE) from August 2002 to June 2016, using long-term remote sensing-derived data of Chl-a and potential driving factors, including total suspended solids (TSS), precipitation, photosynthetically active radiation (PAR), and sea surface temperature (SST); and in situ measurements of potential driving factors, including river discharge, wind speed, alongshore wind (u), cross-shore wind (v), and tidal range. A pixel-by-pixel correlation analysis was conducted to preliminarily examine the relationships between these potential driving factors and Chl-a.
View Article and Find Full Text PDFSci Total Environ
January 2025
Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China; Guangdong Laboratory of Soil Pollution Fate and Risk Management in Earth's Critical Zone and Guangdong Key Laboratory of Contaminated Environmental Management and Remediation, Guangzhou 510045, China.
This study integrated data-driven interpretable machine learning (ML) with statistical methods, complemented by knowledge-driven discrimination diagrams, to identify the primary driving factors of heavy metal (HM) and polycyclic aromatic hydrocarbon (PAH) contamination in agricultural soils influenced by complex sources in a rapidly industrializing region of a megacity in southern China. First, the statistical characteristics of the concentrations of HMs and PAHs, and their correlations with the environmental covariates were explored. Three ML models and a statistical model comprising multiple environmental variable predictors were developed and assessed to predict the concentration of HMs in the agricultural soil.
View Article and Find Full Text PDFSci Total Environ
January 2025
Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research-UFZ, Permoserstr 15, D-04318 Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, D-04103 Leipzig, Germany; Research Data Management-RDM, Helmholtz Centre for Environmental Research GmbH-UFZ, Permoserstraße 15, D-04318 Leipzig, Germany. Electronic address:
The interactions between landscape structure, land use intensity (LUI), climate change, and ecological processes significantly impact hydrological processes, affecting water quality. Monitoring these factors is crucial for understanding their influence on water quality. Remote sensing (RS) provides a continuous, standardized approach to capture landscape structures, LUI, and landscape changes over long-term time series.
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