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Teaching and Learning in Medicine Jun 2024: We aimed to identify the unique challenges and opportunities faced by international student nurses in Türkiye when practicing patient care. This understanding is...
: We aimed to identify the unique challenges and opportunities faced by international student nurses in Türkiye when practicing patient care. This understanding is essential for educators, healthcare institutions, and policy makers to create more inclusive and supportive environments that enhance learning and professional development. Addressing these challenges can lead to better integration of foreign student nurses into the healthcare system, ultimately improving patient care quality. This research is important for all stakeholders in healthcare - educators, administrators, policymakers, and patients - because a diverse and well-supported nursing workforce is essential for the delivery of culturally competent and high-quality care. : This study employed interpretative phenomenology. Data were collected from 12 foreign nursing students from Iraq, Egypt, Syria, Saudi Arabia, Iran, and the Netherlands. Data were collected between 01 and 20 May 2023 in the Nursing Department of the Faculty of Health Sciences of a state university in the province of Şanlıurfa, located in the southeastern region of Türkiye. Data were analyzed using Colaizzi's method. : We identified four themes: "Metaphors describing patient care practices," "Factors affecting care practices," "Needs for education and support," and "Opportunities during patient care practices." Positively influencing factors included better education and living standards and economic benefits, while negatively influencing factors were traumatic events before studying abroad, racial discrimination, language and cultural differences, negative emotions, peer victimization, and lack of use of standards. Interviewees reported a need for training and support and that patient care practices provided opportunities for greater awareness, responsibility, and professional integration. : Positive and negative experiences of foreign student nurses were evident in the delivery of patient care practice. Interventions are needed to alleviate negatively influencing factors, provide training and support for students, and improve opportunities for foreign nationals. Identification of these factors can help medical educators to develop culturally sensitive and inclusive approaches, as well as individual/organisational facilitators that enhance existing opportunities and remove barriers.
PubMed: 38937934
DOI: 10.1080/10401334.2024.2370921 -
Phytochemical Analysis : PCA Jun 2024Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared...
INTRODUCTION
Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI.
OBJECTIVE
Realization of rapid and accurate identification of the origin of G. elata BI.
MATERIALS AND METHODS
Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI.
SAMPLES
Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images.
RESULTS
The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness.
CONCLUSION
Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.
PubMed: 38937551
DOI: 10.1002/pca.3413 -
Academic Radiology Jun 2024Early identification for hematoma expansion can help improve patient outcomes. Presently, there are many methods to predict hematoma expansion. This study compared a...
RATIONALE AND OBJECTIVES
Early identification for hematoma expansion can help improve patient outcomes. Presently, there are many methods to predict hematoma expansion. This study compared a variety of models to find a model suitable for clinical promotion.
MATERIALS AND METHODS
Non-contrast head CT images and clinical data were collected from 203 patients diagnosed with hypertensive intracerebral hemorrhage. Radiomics features were extracted from all CT images, and the dataset was randomly divided into training and validation sets (7:3 ratio) after applying the synthetic minority oversampling method. The radiomics score (Radscore) was calculated using least absolute shrinkage and selection operator (LASSO) regression, combined with selected clinical predictors, to develop a nomogram and four machine learning (ML) models: logistic regression, random forest, support vector machine, and extreme gradient boosting (XGBoost). Discrimination, calibration and clinical usefulness of the nomogram and ML models were assessed.
RESULTS
The nomogram and ML models were integrated with Radscore and clinical predictors. The nomogram demonstrated favorable discriminatory ability in the training set with an AUC of 0.80, which was confirmed in the validation set (AUC=0.76). Among the ML models, the XGBoost model achieved the highest AUC (training set=0.89 and validation set=0.85), surpassing that of the nomogram. The XGBoost model exhibited good clinical usefulness.
CONCLUSION
Both the nomogram and ML models constructed by non-contrast head CT image-based Radscore integrated with clinical predictors can predict early hematoma expansion of hypertensive intracerebral hemorrhage, and the XGBoost model had the highest prediction performance and best clinical usefulness.
PubMed: 38937153
DOI: 10.1016/j.acra.2024.05.035 -
Behavior Therapy Jul 2024Prior research has demonstrated that conducting acquisition in multiple contexts results in more responding to the point that it can even nullify the benefit of...
Prior research has demonstrated that conducting acquisition in multiple contexts results in more responding to the point that it can even nullify the benefit of subsequent extinction in multiple contexts on reducing renewal of excitatory responding. The underlying mechanism to explain why this happens has not been systematically examined. Using self-reported expectancy of the outcome, the current study investigates three mechanisms that potentially explain why acquisition in multiple contexts results in more responding-greater generalization, stronger acquisition learning, or slower extinction learning. Participants (N = 180) received discriminative training with a conditioned stimulus (CS+) and outcome pairing and a CS- → noOutcome pairing in either one or three contexts. This was followed by either extinction treatment in a novel context or no extinction. Finally, testing occurred in the acquisition context, the extinction context, or a novel context. Stronger renewal of extinguished conditioned expectation was observed for participants who received CS+ → Outcome pairings in three contexts relative to one context. There was no effect of the number of contexts on the strength of the excitatory CS+ → Outcome association or degree of inhibitory learning that occurred during extinction. This suggests that generalization is the mechanism responsible for the adverse impact to extinction learning when acquisition is conducted in multiple contexts.
Topics: Humans; Extinction, Psychological; Generalization, Psychological; Male; Female; Young Adult; Conditioning, Classical; Adult; Adolescent; Discrimination Learning
PubMed: 38937046
DOI: 10.1016/j.beth.2023.10.004 -
Nurse Education Today Jun 2024The increasing pursuit of enhanced educational opportunities has led to a significant rise in international student enrollment in various fields, including nursing....
BACKGROUND
The increasing pursuit of enhanced educational opportunities has led to a significant rise in international student enrollment in various fields, including nursing. Nursing is currently in its early stages and faces challenges related to racial microaggression. Understanding the dynamics of racial microaggression is crucial in countries like Turkey, where students from diverse ethnic backgrounds are accommodated.
OBJECTIVE
To explore and achieve a more profound insight into the lived encounters of ethnic minority nursing students confronting racial microaggression.
METHOD
The study employed a descriptive phenomenological approach. Data collection involved conducting in-depth interviews from February 1, 2023, to June 1, 2023. Analysis was performed utilizing Colaizzi's analysis method.
RESULTS
Each participant in the study encountered at least one type of microaggression. The analysis identified three clear themes: "challenges in social interactions," "unfavorable learning atmosphere," and "aspirations for the future."
CONCLUSION
This study highlights the crucial need to establish secure and inclusive environments that foster authentic discussions within academic settings. Faculty and educators should strengthen their ability to consider diverse perspectives in various scenarios. Moreover, integrating an up-to-date and comprehensive curriculum, along with the adoption of inclusive language, into the nursing program is essential for effectively addressing these concerns.
PubMed: 38936040
DOI: 10.1016/j.nedt.2024.106297 -
Journal of Diabetes and Metabolic... Jun 2024The objective of this scoping review was to investigate the effectiveness and limitations of risk prediction models for postpartum glucose intolerance in women with... (Review)
Review
OBJECTIVE
The objective of this scoping review was to investigate the effectiveness and limitations of risk prediction models for postpartum glucose intolerance in women with gestational diabetes mellitus (GDM). The aim was to provide valuable insights for healthcare professionals in the development of robust risk prediction models.
METHODS
A comprehensive literature search was conducted across multiple databases, including PubMed, EBSCO, Web of Science Core Collection, Ovid Full-Text Medical Journal Database, ProQuest, Elsevier ClinicalKey, China National Knowledge Infrastructure, China Biology Medicine, and WanFang Database, spanning from January 1990 to July 2023. To assess the quality of the included models, the Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed.
RESULTS
Fourteen relevant studies were identified and included in the final review, all focusing on model development. The discrimination ability of the included models ranged from 0.725 to 0.940, indicating satisfactory prediction accuracy. However, a notable limitation was that nine of these models (64.3%) did not provide clear guidelines on the selection of potential predictors. Furthermore, only six models (42.86%) underwent internal validation, with none undergoing external validation. A high risk of bias was observed across the included models. Logistic regression, Cox regression, and machine learning were the primary methods employed in the construction of these models.
CONCLUSION
The risk prediction models included in this review demonstrated favorable prediction accuracy. However, due to variations in construction methodologies, direct comparison of their performance is challenging. These models exhibited certain shortcomings, such as inadequate handling of missing data and a lack of internal and external validation, resulting in a high risk of bias. Therefore, it is recommended that these models be updated and externally validated. The development of prospective, multi-center studies is encouraged to construct predictive models with low risk of bias and high clinical applicability, ultimately guiding evidence-based clinical practice.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s40200-023-01330-1.
PubMed: 38932821
DOI: 10.1007/s40200-023-01330-1 -
The European Journal of Neuroscience Jun 2024D-limonene is a widely used flavouring additive in foods, beverages and fragrances due to its pleasant lemon-like odour. This study aimed to investigate the effects of...
D-Limonene reduces depression-like behaviour and enhances learning and memory through an anti-neuroinflammatory mechanism in male rats subjected to chronic restraint stress.
D-limonene is a widely used flavouring additive in foods, beverages and fragrances due to its pleasant lemon-like odour. This study aimed to investigate the effects of D-limonene on the central nervous system when subjected to chronic restraint stress in rats for 21 days. Forty rats were randomly divided into five groups: i) control, ii) D-limonene, iii) restraint stress, iv) restraint stress+D-limonene and v) restraint stress+fluoxetine. Following the induction of restraint stress, the sucrose preference test, the open field test, the novel object recognition test and the forced swimming test were performed. The levels of BDNF, IL-1β, IL-6 and caspase-1 were measured from hippocampal tissue using the ELISA method. Sucrose preference test results showed an increase in consumption rate in the stress+D-limonene and a decrease in the stress group. The stress+D-limonene group reversed the increased defensive behaviour observed in the open-field test compared to the stress group. In the novel object recognition test, the discrimination index of the stress+D-limonene group increased compared to the stress group. BDNF levels increased in the stress+limonene group compared to the stress group. In contrast, IL-1β and caspase-1 levels increased in the stress group compared to the control and decreased in the stress+limonene group compared to the stress group. In this study, D-limonene has been found to have antidepressant-like properties, reducing anhedonic and defensive behaviours and the impairing effects of stress on learning and memory tests. It was observed that D-limonene showed these effects by alleviating neuroinflammation induced by chronic restraint stress in rats.
PubMed: 38932560
DOI: 10.1111/ejn.16455 -
Sensors (Basel, Switzerland) Jun 2024Nowadays, the focus on few-shot object detection (FSOD) is fueled by limited remote sensing data availability. In view of various challenges posed by remote sensing...
Nowadays, the focus on few-shot object detection (FSOD) is fueled by limited remote sensing data availability. In view of various challenges posed by remote sensing images (RSIs) and FSOD, we propose a meta-learning-based Balanced Few-Shot Object Detector (B-FSDet), built upon YOLOv9 (GELAN-C version). Firstly, addressing the problem of incompletely annotated objects that potentially breaks the balance of the few-shot principle, we propose a straightforward yet efficient data clearing strategy, which ensures balanced input of each category. Additionally, considering the significant variance fluctuations in output feature vectors from the support set that lead to reduced effectiveness in accurately representing object information for each class, we propose a stationary feature extraction module and corresponding stationary and fast prediction method, forming a stationary meta-learning mode. In the end, in consideration of the issue of minimal inter-class differences in RSIs, we propose inter-class discrimination support loss based on the stationary meta-learning mode to ensure the information provided for each class from the support set is balanced and easier to distinguish. Our proposed detector's performance is evaluated on the DIOR and NWPU VHR-10.v2 datasets, and comparative analysis with state-of-the-art detectors reveals promising performance.
PubMed: 38931667
DOI: 10.3390/s24123882 -
Life (Basel, Switzerland) May 2024The supraspinatus tendon is one of the most involved tendons in the development of shoulder pain. Extracorporeal shockwave therapy (ESWT) has been recognized as a valid...
The supraspinatus tendon is one of the most involved tendons in the development of shoulder pain. Extracorporeal shockwave therapy (ESWT) has been recognized as a valid and safe treatment. Sometimes the symptoms cannot be relieved, or a relapse develops, affecting the patient's quality of life. Therefore, a prediction protocol could be a powerful tool aiding our clinical decisions. An artificial neural network was run, in particular a multilayer perceptron model incorporating input information such as the VAS and Constant-Murley score, administered at T0 and at T1 after six months. It showed a model sensitivity of 80.7%, and the area under the ROC curve was 0.701, which demonstrates good discrimination. The aim of our study was to identify predictive factors for minimal clinically successful therapy (MCST), defined as a reduction of ≥40% in VAS score at T1 following ESWT for chronic non-calcific supraspinatus tendinopathy (SNCCT). From the male gender, we expect greater and more frequent clinical success. The more severe the patient's initial condition, the greater the possibility that clinical success will decrease. The Constant and Murley score, Roles and Maudsley score, and VAS are not just evaluation tools to verify an improvement; they are also prognostic factors to be taken into consideration in the assessment of achieving clinical success. Due to the lower clinical improvement observed in older patients and those with worse clinical and functional scales, it would be preferable to also provide these patients with the possibility of combined treatments. The ANN predictive model is reasonable and accurate in studying the influence of prognostic factors and achieving clinical success in patients with chronic non-calcific tendinopathy of the supraspinatus treated with ESWT.
PubMed: 38929665
DOI: 10.3390/life14060681 -
Life (Basel, Switzerland) May 2024Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions....
Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.
PubMed: 38929638
DOI: 10.3390/life14060652