mdpi-ai/citelens huggingface dataset:
The effect of questioning gender stereotype threat on girl’s standing long jump performance
Field: PSYCHOLOGY

Over the past few years, the sport psychology literature has established that gender stereotype threat (ST) is one of the factors that can impair girls’ performance. However, few studies have attempted to annihilate these negative effects. The purpose of the current study was to investigate whether questioning gender ST can mitigate the classical decline in girls’ standing long jump (SLJ) performance. The participants were 120 girls (Mage = 10.74 ± 0.85 years), selected through convenience sampling and randomly assigned to three groups: the gender ST group (n = 40), the questioning group (n = 40), and the control group (n = 40). For all groups, baseline performance (i.e., SLJ) was measured by a female researcher following a warm-up period. In the experimental phase, the control group repeated the baseline conditions; the gender ST group completed the same test but was evaluated by a male examiner (i.e., implicit stereotype induction), while participants in the questioning group were assessed after receiving questioning statements while performing the task in front of a male examiner. The results of the present study showed that the induction of a gender ST leads to a decrease in SLJ in girls. Additionally, if these inducing conditions of gender ST are accompanied by a questioning condition, the negative effects of gender ST can be reduced, and SLJ in girls does not decline. Based on our findings, this intervention is recommended as a simple, inexpensive, and quick solution for mitigating the negative effects of gender ST on girl’s motor performance.
The effects of digital CBT intervention on attentional bias and sleep quality of poor sleepers with insomnia symptoms
Field: PSYCHOLOGY

Attentional bias is a salient manifestation of insomnia. Digital cognitive therapy for insomnia (dCBT-I) has been validated as effective in alleviating this cognitive dysfunction. However, the effect of dCBT-I on attentional bias among Chinese individuals with insomnia remains undiscussed. This research sought to investigate this effect via a pictorial dot-probe task. In Study 1, the pattern of attentional bias among poor sleepers (N = 52) and normal sleepers (N = 56) was assessed by the dot-probe task. In study 2, dCBT-I and conventional education were received by the experimental group (N = 42) and control group (N = 25), respectively. The dot-probe tasks and sleep quality assessments were completed at baseline and post-test. The results of Study 1 indicated that poor sleepers exhibited significant attentional bias, characterized by increased attentional vigilance. Compared to normal sleepers, they showed heightened attentional vigilance toward sleep-related cues. The results of Study 2 showed that both dCBT-I and conventional education led to improvements in PSQI scores. However, only dCBT-I training alleviated attentional vigilance toward sleep-related cues. Additionally, dCBT-I was uniquely effective in reducing feelings of fatigue. Poor sleepers had a significant attentional bias, marked by heightened vigilance toward sleep-related cues. Digital CBT-I effectively reduced attentional vigilance and fatigue, suggesting that dCBT-I targets the cognitive distortions associated with insomnia. ChiCTR2100053172 (registered 13/11/2021).
Examining the Factor Structure and Validity of the Depression Anxiety Stress Scale-21
Field: PSYCHOLOGY

Background: The prevalence of mental health disorders calls for valid and reliable instruments that are easy to administer and assess for clinicians and researchers. The Depression Anxiety Stress Scale-21 (DASS-21) is a commonly used instrument to assess psychological distress; however, model fit and internal reliability issues have been reported. Our objective was to assess the factorial and structural validity of the DASS-21. Methods: A confirmatory factor analysis (CFA) was conducted on the full sample (n = 1036) to assess the proposed three-factor DASS-21 using a priori cut-off values. Because model fit indices were not met, an exploratory factor analysis (EFA) was conducted to identify a parsimonious model. The resulting three-factor structure (i.e., DASS-9) was then assessed using CFA and multigroup invariance testing procedures. Results: The proposed three-factor DASS-21 did not meet model fit criteria. The DASS-9 did meet recommended model fit criteria and was invariant between sex, injury status, mental health diagnosis, and activity level groups. Statistically different group means were found between mental health diagnosis and activity level groups, while no differences between sex or injury status groups were found. Conclusions: The current study provides support to use a condensed DASS-21 instrument, such as the DASS-9. Future research is necessary to establish the DASS-9 prior to its adoption in research and clinical practice. Additionally, there is a need to identify and review all condensed versions of the DASS-21, so individuals know which instrument can be used for clinical or research purposes.
Page: investigating the predictors of general psychological help seeking intention among people who attempted suicide by using structural equation modeling
Field: PSYCHOLOGY

The aim of this study was to determine the role of suicide literacy, suicide stigma, perceived social support, and attitudes toward seeking professional psychological help (ATSPPH) in predicting general help-seeking intention among individuals who have attempted suicide by structural equation modeling. This cross-sectional study was conducted among 462 people who were referred to the hospital due to suicide attempt in one of the cities of eastern Iran in 2023. The sampling method in this study was consecutive sampling. The Pearson correlation, One-way ANOVA, and Independent-samples t-test were used to analyze data by SPSS software. Also, AMOS software was used for conducting structural equation modeling and checking the standardized direct effects and standardized indirect effects between variables. Only 0.9% (n = 4) of participants answered correctly more than 17 questions regarding suicide literacy. The structural equation modeling (SEM) results showed that suicide literacy, suicide stigma, perceived social support, and ATSPPH predicted 51% variance in the general help-seeking intention (R2 = 0.51, PGFI = 0.607, PCFI = 0.568, RMSEA = 0.064). The variables of suicide literacy (estimate total effect = 0.615), ATSPPH (estimate total effect = 0.368), perceived social support (estimate total effect = 0.123), and suicide stigma (estimate total effect = 0.033) had the greatest impact in predicting the general help-seeking intention. The SEM results highlight the importance of paying more attention to suicide literacy, reducing suicide stigma, promoting social support, improving the positive attitude toward mental health services, encouraging people who have attempted suicide to seek psychological help, and finally preventing suicide attempt.
Essential Elements for Implementing AI Tools in Elementary School: A Systematic Literature Review
Field: EDUCATION

The global use of Artificial Intelligence (AI) has attracted considerable attention, and its integration into educational systems is a priority that warrants further exploration. In collaboration with UNESCO, numerous organizations have proposed parameters advocating for the inclusion of AI in basic education systems. A systematic literature review (SLR) was conducted to identify these parameters from the existing research. Although these parameters have been mentioned in some studies, they are generally not prioritized in the research landscape. AI tools are primarily used to support students, while teachers typically employ a pedagogical approach centered on in-class activities. Additionally, essential conditions related to research requirements and involvement from the private and third sectors showed consistent adherence across the examined studies. However, it was found that only 52% of the studies included an ethical declaration regarding the data collected by AI during research development, especially regarding studies involving children. This review provides a guide for educational communities looking to enhance pedagogical practices through AI integration into their educational environments, but who may be uncertain about where to begin. Questions related to AI modality selection, pedagogical relevance, ethical considerations, and procedural guidelines for integrating AI into curricula are addressed through the insights provided in this review.
Advancing Healthcare: Intelligent Speech Technology for Transcription, Disease Diagnosis, and Interactive Control of Medical Equipment in Smart Hospitals
Field: AI

Intelligent Speech Technology (IST) is revolutionizing healthcare by enhancing transcription accuracy, disease diagnosis, and medical equipment control in smart hospital environments. This study introduces an innovative approach employing federated learning with Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) neural networks to improve IST performance. Leveraging the “Medical Speech, Transcription, and Intent” dataset from Kaggle, comprising a variety of speech recordings and corresponding medical symptom labels, noise reduction was applied using a Wiener filter to improve audio quality. Feature extraction through MLP and sequence classification with GRU highlighted the model’s robustness and capacity for detailed medical understanding. The federated learning framework enabled collaborative model training across multiple hospital sites, preserving patient privacy by avoiding raw data exchange. This distributed approach allowed the model to learn from diverse, real-world data while ensuring compliance with strict data protection standards. Through rigorous five-fold cross-validation, the proposed Fed MLP-GRU model demonstrated an accuracy of 98.6%, with consistently high sensitivity and specificity, highlighting its reliable generalization across multiple test conditions. In real-time applications, the model effectively performed medical transcription, provided symptom-based diagnostic insights, and facilitated hands-free control of healthcare equipment, reducing contamination risks and enhancing workflow efficiency. These findings indicate that IST, powered by federated neural networks, can significantly improve healthcare delivery, accuracy in patient diagnosis, and operational efficiency in clinical settings. This research underscores the transformative potential of federated learning and advanced neural networks for addressing pressing challenges in modern healthcare and setting the stage for future innovations in intelligent medical technology.
Empirical Evaluation and Analysis of YOLO Models in Smart Transportation
Field: AI

You Only Look Once (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models, selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (i.e., COCO and PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications. Third, multiple Intersection over Union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. It was found that YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.
Negative Association Between Schizophrenia and Subsequent Cancer Diagnoses—A Retrospective Cohort Study from Germany
Field: PSYCHOLOGY

Background: Since previous studies have reported contradictory findings regarding the relationship between schizophrenia and cancer, we evaluated the association between schizophrenia and cancer diagnoses. Methods: In this retrospective cohort study, the IQVIA Disease Analyzer database was utilized to examine the incidence of cancer among patients aged over 18 years diagnosed with schizophrenia in German general practices from 2005 to 2022. Patients with schizophrenia were compared with those without the condition, with adjustments made for age, sex, index year of diagnosis, average annual practitioners visit frequency, and comorbidity. Kaplan–Meier curves were used to analyze the 10-year cumulative incidence of schizophrenia and cancer in total amongst patients with and without schizophrenia. Univariate Cox regression analysis was performed to calculate Hazard Ratios (HR) of cancer risk and their 95% confidence intervals (CI) of cancer in total and of specific cancer types. Results: Patients with schizophrenia (N = 13.711) had a lower incidence of cancer diagnosis compared to those without (N = 68.555). Specifically, 10.4% of patients with schizophrenia and 12.5% of patients without the condition were diagnosed with cancer (p < 0.001). Cox regression analysis showed a significant association between schizophrenia and subsequent cancer in the total population (HR: 0.82; 95% CI: 0.76–0.90), and among men (HR: 0.70; 95% CI: 0.61–0.80), but not among women (HR: 0.94, 95% CI: 0.84–1.04). Analyses stratified by cancer type and sex revealed a strong and significant association between schizophrenia and a decreased risk of prostate cancer in men (HR: 0.38; 95% CI: 0.24–0.61). Furthermore, there was also a negative association between schizophrenia and colorectal cancer risk in men, but statistical significance was not reached (HR: 0.58; 95% CI: 0.37–0.93). Conclusions: This study demonstrates negative associations between schizophrenia and subsequent cancer, and more specifically in men for prostate and colorectal cancer. However, further research is required to explore the underlying reasons for these associations.
Exploring the utilization and deficiencies of Generative Artificial Intelligence in students’ cognitive and emotional needs: a systematic mini-review
Field: AI

Despite advances in educational technology, the specific ways in which Generative Artificial Intelligence (GAI) and Large Language Models cater to learners’ nuanced cognitive and emotional needs are not fully understood. This mini-review methodically describes GAI’s practical implementations and limitations in meeting these needs. It included journal and conference papers from 2019 to 2024, focusing on empirical studies that employ GAI tools in educational contexts while addressing their practical utility and ethical considerations. The selection criteria excluded non-English studies, non-empirical research, and works published before 2019. From the dataset obtained from Scopus and Web of Science as of June 18, 2024, four significant studies were reviewed. These studies involved tools like ChatGPT and emphasized their effectiveness in boosting student engagement and emotional regulation through interactive learning environments with instant feedback. Nonetheless, the review reveals substantial deficiencies in GAI’s capacity to promote critical thinking and maintain response accuracy, potentially leading to learner confusion. Moreover, the ability of these tools to tailor learning experiences and offer emotional support remains limited, often not satisfying individual learner requirements. The findings from the included studies suggest limited generalizability beyond specific GAI versions, with studies being cross-sectional and involving small participant pools. Practical implications underscore the need to develop teaching strategies leveraging GAI to enhance critical thinking. There is also a need to improve the accuracy of GAI tools’ responses. Lastly, deep analysis of intervention approval is needed in cases where GAI does not meet acceptable error margins to mitigate potential negative impacts on learning experiences.
Clinical decision making and moral distress among intensive care units nurses in Iran
Field: PSYCHOLOGY

Intensive care units are often presented as environments where ethical issues are common and decisions can determine the life or death of patients, and these units have unique challenges due to critical health care. In these units, the relationship between the medical team and the patient’s relatives, their refusal of treatment, informed consent causes the nurses to have conflict in their decision making, therefore, this study aims to determine the level of clinical decision-making and moral distress and the relationship between them in intensive care units nurses. This cross-sectional study was conducted with a descriptive-analytical approach in 2023 in Gorgan city. The number of 198 nurses in the intensive care units of Gonbad Kavos hospitals in the north of Iran were investigated and evaluated using the Corley Moral Distress questionnaire (2002), and Laurie’s Clinical Decision questionnaire (2001) in 2023. Independent T-test and anova analysis of variance were used for bivariate analysis. The significance level in this study was considered 0.05. The results of the study showed that the mean and standard deviation of clinical decision making was 60.98 ± 10.25 (analytical-systematic level) and moral distress was 92.2 ± 23.61 (moderate level). There was a statistically significant relationship between clinical decision-making and nurses’ moral distress (P < 0.001 and r = 0.370). The moral distress score had a statistically significant relationship with marriage, employment status, education level, age, work experience and duration of employment in the special department. Also, the clinical decision score had a statistically significant relationship with employment status, education level, age and work experience. According to these results, it seems that more attention should be paid to the moral distress of nurses in intensive care units, and it is necessary to improve their decision-making towards intuitive interpretation, which is done by designing and compiling training programs and workshops for nurses can be done, so that we can provide optimal nursing services to the patients of this department.
Prevalence of Abnormalities at Tandem Endoscopy in Patients Referred for Colorectal Cancer Screening/Surveillance Colonoscopy
Field: ONCOLOGY

Introduction: Performing a tandem endoscopy and colonoscopy in selected individuals has advantages, such as the early detection of benign and/or precancerous foregut diseases; it is efficient, and it may allow added therapies. It may also have disadvantages, such as generating anxiety from false-positive screening, possible harm from further testing, and unproven cost-effectiveness. Aims: We aimed to examine the prevalence of foregut endoscopic and histologic abnormalities in subjects referred for screening/surveillance colonoscopy who also underwent a tandem endoscopy. We wanted to (1) assess implications for cancer detection, intervention, and surveillance of precancerous foregut abnormalities, (2) identify benign foregut lesions, and (3) generate data on the utilities of this tandem approach. Patients and Methods: A retrospective cohort study of consecutive subjects referred for screening or surveillance colonoscopy who also underwent an endoscopy. Based on national screening guidelines, responses to prompting questions, personal or family history, or other risk factors, subjects were assigned to tandem endoscopy with biopsies (modified Seattle and Sydney protocols), under one anesthesia. Results: Of the 1004 patients referred for colonoscopy, 317 (32%) underwent tandem endoscopy. There were 214 women and 103 men. There were 237 Whites, 16 Asians, 40 Blacks, and 24 Hispanics. Median age was 59 (range 19–85). At endoscopy, we identified actionable benign (45%) peptic, inflammatory, and H. pylori-related abnormalities, and premalignant findings (i.e., intestinal metaplasia, 27%, dysplasia, 2%, and cancer 0.9%), comparable to the premalignant (40.3%) and malignant (0.6%) colonoscopy yield. Conclusions: When implemented based on national screening guidelines, tandem EGD and colonoscopy combines Barrett’s esophagus and gastric cancer screening in one examination, and it has a high yield in a diverse US population.
Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability
Field: AI

Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively model intricate relationships between numerical and categorical variables, resulting in a notable increase in prediction accuracy. Methods: A comparative analysis was performed with other Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Darknet, and Extreme Inception (Xception), showing superior performance of the proposed Resnet. Key factors influencing accident severity were identified, with Shapley Additive Explanations (SHAP) values helping to address the need for transparent and explainable Artificial Intelligence (AI) in critical decision-making areas. Results: The generalizability of the ResNet model was assessed by training it, initially, on a UK road accidents dataset and validating it on a distinct dataset from India. The model consistently demonstrated high predictive accuracy, underscoring its robustness across diverse contexts, despite regional differences. Conclusions: These results suggest that the adapted ResNet model could significantly enhance traffic safety evaluations and contribute to the formulation of more effective traffic management strategies.