Background: Gender stereotyping of academic domains has long been a major issue in education. However, previous research has mainly focused on male-dominated fields and women's disadvantage in such fields. Little attention has been paid to the fields of study, such as foreign language learning, which are typically stereotyped as female domains.
Aims: This study aimed to investigate whether relations between (1) learners' gender stereotypes about English as a foreign language (EFL) learning and language attainment and (2) learner perceptions of teacher stereotypes of EFL learning and language attainment were mediated by anxiety and self-efficacy.
Sample: Data were collected from 701 university students (M = 19.7 years, 49.4% male) learning EFL in three Turkish universities.
Method: Data were collected over three waves. Multi-group structural equation modelling approach was used to analyse the data.
Results: Results showed the relations between learners' gender stereotypes about EFL learning, and language attainment were mediated by self-efficacy. Self-efficacy also mediated the relationship between learner perceptions of teacher stereotypes of EFL learning and language attainment, but only for women. Language anxiety was not a mediator between gender stereotypes and attainment in either model tested.
Conclusions: Findings show that gender stereotypes about EFL learning might affect learners' language attainment by altering their self-efficacy. Helping learners to maximise their self-efficacy will therefore be beneficial for their language attainment.
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http://dx.doi.org/10.1111/bjep.12446 | DOI Listing |
Background: Formerly incarcerated individuals (FIIs) encounter difficulties with covering the cost of dental and medical care, adhering to medication regimens, and receiving fair treatment from health care providers. Yet, no published research has examined modifiable pathways to increase FIIs' health literacy (HL), which is essential for addressing the health needs of this vulnerable population.
Objective: The aim of this article is to examine neighborhood characteristics (neighborhood deprivation, racial and economic polarization, and residential segregation) and public assistance program enrollment as structural determinants of limited health literacy (LHL) among FIIs.
Taiwan J Ophthalmol
November 2024
Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand.
Recent advances of artificial intelligence (AI) in retinal imaging found its application in two major categories: discriminative and generative AI. For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. Vision transformers (ViT), inspired by the transformer architecture in natural language processing, has emerged as useful techniques for discriminating retinal images.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
School of Computer Science, University College Dublin (UCD), D04 V1W8 Dublin, Ireland.
Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein folding and function within cells by predicting protein secondary structures. The advent of deep learning models, capable of processing complex sequence data and identifying meaningful patterns, offer substantial potential to enhance the accuracy and efficiency of protein structure predictions.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Emergency Medicine, Arrowhead Regional Medical Center, 400 N. Pepper Ave, Colton, CA, 92324, USA.
Background: Large language models (LLMs) such as ChatGPT-4 (CG4) are proving to be valuable tools in the medical field, not only in facilitating administrative tasks, but in augmenting medical decision-making. LLMs have previously been tested for diagnostic accuracy with expert-generated questions and standardized test data. Among those studies, CG4 consistently outperformed alternative LLMs, including ChatGPT-3.
View Article and Find Full Text PDFAdv Med Educ Pract
December 2024
Queen Charlotte's and Chelsea Hospital, Imperial College, London, UK.
Purpose: To determine the level of uptake of telemedicine among postgraduate obstetrics and gynaecology (O&G) trainees in London, and how they perceive its impact on their training.
Methods: A mixed-methods survey aimed at exploring trainee perspectives of telemedicine use in clinical practice and its implications for training. Study participants were O&G specialist doctors on the London (UK) training programme.
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