Balanced Representation Research Articles

Sex differences in brain MRI using deep learning toward fairer healthcare outcomes

This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Center for Aging and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl.

Abstract 4141488: Initial Real-World Experience with a Novel Wearable Cardioverter Defibrillator

Background: The wearable cardioverter defibrillator (WCD) can reduce sudden cardiac death; effectiveness depends on adherence. A novel WCD designed for sex specific comfort and noise immunity has reported improved compliance. Hypothesis: Patient compliance with the WCD maximizes device utility in treating significant arrhythmia. Aims: Report WCD patient compliance and efficacy in a cohort with balanced gender representation. Evaluate WCD performance in minimizing noise artifact leading to false alarms and unnecessary shocks. Methods: Retrospective analysis of patients prescribed the ASSURE WCD at the Cleveland Clinic. Clinical data was collected via medical chart review. All device-detected arrhythmia episodes were manually adjudicated by a Board-certified Electrophysiologist. Detected rhythms that persist, result in an audible alarm notifying that a shock is imminent. A conscious patient can manually divert a shock. An alarm was deemed true and shock appropriate if the underlying rhythm was sustained VT/VF. Results: Fifty-five patients were fitted with a WCD. Median daily use 22 hr/day (IQR 13,24) and median duration of use 43 days (IQR 8,79). Female representation was 49%, significantly greater than prior reports of WCD experience. 4 episodes of sustained VT/VF were detected in 4 patients. A single shock terminated VT/VF in 2, while spontaneous termination of VT/VF occurred in the other 2 patients who diverted shocks. One shock for hemodynamically unstable atrial fibrillation with heart rate >200bpm was delivered and deemed necessary. No deaths occurred while wearing a WCD. Of 163 recorded episodes, 62 (38%) persisted and triggered an alarm (Figure 1). 10 patients experienced a median of 1 (IQR 1,9.5) alarm during the WCD wear period. The majority (97%) of alarms were associated with a significant tachyarrhythmia, with only 3% due to noise artifact. No inappropriate, unnecessary shocks were delivered. Discussion: Patient compliance with the ASSURE WCD was high in this real-world observational study with equal representation of both sexes. The WCD effectively terminated sustained hemodynamically significant tachyarrhythmias, emitted few false alarms due to noise and resulted in no unnecessary and inappropriate shocks.

A Netnographic Study on The Reasons Why People Fail to Learn English

In a global and digital world, learning a language, especially English is essential for people to keep up with the developments in science, business, technology, etc. Despite starting to learn English early, students might not always reach desired proficiency levels. Therefore, the present study aims to find out some of the reasons why people fail to learn English. Employing a netnographic approach, the researchers examined asynchronous comments posted in online communities on a social media platform. Within the scope of this study, 198 comments answering the question of why people fail to learn English were obtained from three online groups of English language teachers on Facebook. The findings show that 154 out of 198 comments aimed to answer the question. They were analyzed through content analysis, which revealed that there are educational, social, personal, and linguistic reasons why people fail to learn English. Educational reasons were among the most mentioned reasons by the members of the communities. In addition, the analysis indicates a balanced representation of linguistic and personal factors, with social factors slightly less prominent in comparison. Considering the existing literature review, several implications are highlighted to overcome the difficulties and challenges of learning English. Finally, this study is limited by its reliance on Facebook posts collected within a specific timeframe; future research could enhance depth and scope by incorporating longitudinal methods, diverse social media platforms, and in-depth interviews.

Long-term trends of publications in journal of neuro-oncology: a bibliographic study of a core journal in the field of neuro-oncology.

The Journal of Neuro-Oncology (JNO), established in 1983, plays a key role in publishing research on brain and spinal cord tumors. This study examines JNO's publication trends, focusing on country and gender representation to highlight its global impact. Statistical analyses were conducted using R. Gender of the first authors was predicted using a gender-guesser, and author affiliations were used to determine publication countries. We introduced a novel Country-Related Diversity (CRD) index to assess the JNO's representativeness, comparing a country's JNO publications to its overall neurosurgical output. An index value of 1 indicates proportional representation. The JNO corpus, spanning from 1983 to 2024, comprises 8,154 documents with an average document age of 14.4 years. The average citation count per document is 28.71, with a rate of 2.16 citations per document per year. JNO's scientific output has grown significantly, peaking at 397 articles in 2011, with a long-term annual growth rate of 3.7%. The keyword analysis highlights "glioblastoma" as the most frequent term, reflecting the journal's neuro-oncological focus. Geographically, the U.S. led with 2,535 articles (40.1%), followed by China and Germany. International collaboration rose steadily, with multi-country publications increasing from 4.76% in 1983 to 20.98% in 2024. Analyzing contributions from different countries showed a converging CRD index toward 1 (P < 0.01), with U.S. and non-U.S. countries trending similarly. Upper-middle-income countries displayed fluctuating CRD patterns, whereas lower-middle-income countries lagged behind. Authorship analysis showed an increasing trend in co-authorship (P < 0.01), with the average number of authors per paper reaching 10.4 by 2024. Gender representation revealed a growing number of female first and senior authors, although males still dominate. By 2024, 32.9% of first authors and 21.6% of senior authors were female, signaling a gradual trend toward gender parity (P < 0.01). The CRD index offers a standardized measure of country-specific research representation in the JNO. The convergence towards 1 reflects balanced international representation. JNO publication also reflects a trend toward gender equity, with a notable rise in female first authors, enhancing global research inclusivity.

Cultural Persons’ Representations in High School English Language Teaching Materials in Indonesia

Integrating both local and target cultures in English Language Teaching Materials and emphasizes the need for balance to promote cultural diversity and understanding become an important thing in today’s era. During teaching practice activities at SMAN 17 Tebo, the researcher found that the cultural materials in the English Teaching Materials used are not balanced between the source culture, target culture, and international culture. This has an impact on cross-cultural understanding and less than optimal cultural awareness among students. The purpose of this research is to explore the representation of cultural persons in government textbook and teacher-made modules. This research uses qualitative content analysis as a research design. This research involved a sample of government-approved English language textbooks entitled “Bahasa Inggris Work in Progress” and a teacher-made module at SMAN 11 Tebo. The results show that from the theme of great athletes in chapter one of the Bahasa Inggris Work In Progress textbook and teacher-made module, it can be seen that the dominant sports branch comes from international culture. While the cultural persons represented as athletes are dominated by the source culture. It is recommended that future development of English language teaching (ELT) materials, both in government textbooks like Bahasa Inggris Work in Progress and teacher-made modules, should strive for a more balanced representation of source culture, target culture, and international culture for fostering intercultural competence among Indonesian students.

Applying Bayesian Networks for Detecting Bank Fraudulent Transactions in Nigeria

Bank fraud is an increasingly prevalent issue, causing substantial financial losses for both financial institutions and customers. Also, with the increasing prevalence of bank fraud, especially facilitated by online banking and digital payments, there is a pressing need for potential expertise deployment and advanced fraud detection techniques in real-world banking systems. This study specified the Bayesian Network models for detecting fraudulent bank transactions. Bayesian Networks offer a probabilistic graphical modeling approach that can effectively capture complex relationships and dependencies within financial data. Thus, the research aimed at developing a custom Bayesian network model trained on a large dataset of bank transactions comprising over one million bank transactions to classify instances as fraudulent or non-fraudulent. Python 3.11.10 was used for the graphical representations. Down-sampling was employed to reduce the dataset from its initial size of 1 million observations to 17,650 observations, ensuring a balanced representation of both fraud and non-fraud instances. Various estimation techniques: Maximum Likelihood, Bayesian Networks, and Expectation Maximization were employed and evaluated to learn the model parameters. The model's performance was measured using metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The Bayesian Networks estimator achieved an overall accuracy of 66.18%, precision of 67.73%, F1-score of 64.06%, ROC-AUC of 66.13%, Recall of 60.77%, Sensitivity of 60.77% and Specificity of 71.50%. Also, the Maximum Likelihood achieved an overall Accuracy of 66.77%, Precision of 69.22%, F1-score of 63.96%, ROC-AUC of 66.71%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 73.97%. Likewise, the Expectation Maximization achieved an overall Accuracy of 66.83%, Precision of 69.31%, F1-score of 64.00%, ROC-AUC of 66.77%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 74.09%. On the confusion matrix, the model correctly classified 1272 instances as Non-Fraudulent transactions (True Negative). Also, it was observed that the model incorrectly classified 507 instances as fraudulent transactions when they were Non-Fraudulent (False Positive). The model similarly incorrectly classified 687 instances as Non-Fraudulent transactions when they were Fraudulent (False Negative). Finally, it correctly classified 1064 instances as fraudulent transactions (True Positive). These results demonstrated the Bayesian Network's ability to identify fraudulent transactions while minimizing false alarms accurately. The findings highlight the potential of Bayesian Networks as a robust framework for fraud detection in the banking sector, contributing to enhanced security and reduced financial losses. The developed model can be introduced into existing banking systems to strengthen fraud prevention strategies.

Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study.

The field of mental health technology presently has significant gaps that need addressing, particularly in the domain of daily monitoring and personalized assessments. Current noninvasive devices such as wristbands and smartphones are capable of collecting a wide range of data, which has not yet been fully used for mental health monitoring. This study aims to introduce a novel dataset for personalized daily mental health monitoring and a new macro-micro framework. This framework is designed to use multimodal and multitask learning strategies for improved personalization and prediction of emotional states in individuals. Data were collected from 298 individuals using wristbands and smartphones, capturing physiological signals, speech data, and self-annotated emotional states. The proposed framework combines macro-level emotion transformer embeddings with micro-level personalization layers specific to each user. It also introduces a Dynamic Restrained Uncertainty Weighting method to effectively integrate various data types for a balanced representation of emotional states. Several fusion techniques, personalization strategies, and multitask learning approaches were explored. The proposed framework was evaluated using the concordance correlation coefficient, resulting in a score of 0.503. This result demonstrates the framework's efficacy in predicting emotional states. The study concludes that the proposed multimodal and multitask learning framework, which leverages transformer-based techniques and dynamic task weighting strategies, is superior for the personalized monitoring of mental health. The study indicates the potential of transforming daily mental health monitoring into a more personalized app, opening up new avenues for technology-based mental health interventions.

Lexical Analysis of Japanese Children’s Songs from the Perspective of Japanese Language Education

Japanese children's songs can be a valuable resource for adult learners of the language, Firstly, the vocabulary used in children's songs is predominantly high-frequency and foundational, with a large portion falling below the N2 proficiency level. By mastering the words and phrases found in these songs, learners can expand their conversational abilities beyond the confines of traditional textbooks. Secondly, the songs offer a balanced representation of positive, negative, and neutral emotions, reflecting the emotional range and nuances of the Japanese language. This exposure to diverse emotional expressions can enrich learners' understanding of language and cultural nuances. Moreover, the study's semantic analysis reveals frequent word associations and co-occurrences, which can help learners grasp the interconnectedness of Japanese vocabulary and the cultural contexts in which words are used. Importantly, children's songs are deeply rooted in Japanese culture, indirectly exposing learners to various cultural aspects, such as the appreciation of nature, the concept of transience, and the importance of harmony and interconnectedness. This cultural immersion can foster a deeper understanding and appreciation of Japanese values and traditions. Furthermore, the familiar melodies and sing-along nature of these songs can facilitate social bonding and interpersonal connections among learners and native speakers. Engaging in shared cultural experiences, such as singing nursery rhymes together, can create a sense of community and strengthen the bond between learners and the target language culture.

Comparison of Analgesia Methods Through a Web Platform in Patients Undergoing Thoracic Surgery: Pilot Design, Implementation, and Validation Study.

Pain management is a vital and essential part of postoperative pectus excavatum (PE) care. Given the lack of an international consensus on guidelines for postoperative handling and evaluation, further research is necessary to compare the efficacy of existing pain management methods regarding pain relief, side effects, and long-term outcomes. In this context, the use of eHealth solutions for data mining can enhance data collection efficiency, reduce errors, and improve patient engagement. However, these digital health care frameworks are currently underused in the context of pain management for PE. This research is part of the broader Cryoanalgesia for Pain Management After Pectus Excavatum Repair (COPPER) study conducted by Giannina Gaslini Children's Hospital to address postoperative pain and recovery in PE patients treated with either standard thoracic epidural analgesia or cryoanalgesia, which is considered its innovative alternative approach. Specifically, this work is aimed at introducing a valuable tool for a comprehensive and quantitative comparison of the 2 analgesia strategies. The tool is a web and mobile app designed to facilitate data collection, management, and analysis of clinical data for pain assessment. The adopted approach involves a careful design based on clinician input, resulting in an intuitive app structure with 3 main screens. Digital surveys are borrowed from paper surveys, including medical history and preoperative, postoperative, and follow-up evaluations. XTENS 2.0 was used to manage the data, and Ionic facilitated cross-platform app development, ensuring secure and adaptable data handling. Preliminary analysis on a pilot cohort of 72 patients (36 treated with standard therapy and 36 treated with cryoanalgesia) indicated successful patient enrollment and balanced representation across treatment groups and genders. Notably, hospital stay was significantly shorter with cryoanalgesia than with standard therapy (Mann-Whitney-Wilcoxon 2-sided test with Bonferroni correction; P<.001; U statistic=287.5), validating its treatment efficacy. This work is a step toward modernizing health care through digital transformation and patient-centered models. The app shows promise in streamlined data collection and patient engagement, although improvements in multilingual support, data validation, and incentivization of questionnaire completion are warranted. Overall, this study highlights the potential of digital health solutions in revolutionizing health care practices, fostering patient involvement, and improving care quality.

Customizing Yoga for Global Diversity: A Comparative Study of Eastern and Western Practices for Effective Integration Into Complementary Healthcare.

This cross-sectional study aimed to examine differences in yoga practices to enhance the validity and comparability of yoga research findings. It addresses the "black-box" approach to yoga interventions by highlighting various delivery components that can impact the validity of outcomes. By moving away from the generalization of yoga outcomes, this study provides deeper insights into yoga practices across culturally diverse populations. These insights are crucial for improving the reliability of outcomes and facilitating the integration of yoga into global complementary healthcare. The study included 2,619 participants, with a balanced representation from India (1,296) and the United States (1,323). The participants were diverse, with 47.5% being yoga masters, gurus, therapists, and experts. The study utilized the 14 subscales of the Essential Properties of Yoga Questionnaire (EPYQ) to assess four factors of yoga practice: region (India/United States), sex (male/female), delivery mode (remote/in-person), and participation status (instructor/practitioner). The EPYQ demonstrated strong internal consistency (Cronbach's alpha = 0.928). The total EPYQ score was higher in India than in the United States. Significant differences (p < 0.05) were observed for the 14 subscales across all four factors: region, sex, delivery mode, and participation status. Notable observations include that, by region, the United States sample scored higher than the Indian sample on the subscales of body awareness and acceptance/compassion (mean differences of -0.06 and -0.03, respectively), while the Indian sample scored higher on the spirituality and social aspects subscales (mean differences of 0.54 and 0.57, respectively). The remote delivery mode scored higher for the subscales of yoga philosophy and health benefits (mean differences of 0.17 and 0.13, respectively), and lower for the subscales of individual attention and active postures (mean differences of -0.17 and -0.04, respectively). By sex, the scores on the subscales of social aspects and yoga philosophy were higher for men than for women (mean differences of -0.26 and -0.24, respectively), whereas women scored higher for the subscales of body awareness and restorative postures (mean differences of 0.19 and 0.17, respectively). By participation status, practitioners showed higher scores for the subscales of physicality and active postures (mean differences of -0.05 and -0.02, respectively), whereas instructors scored higher on the subscales of yoga philosophy and meditation/mindfulness (mean differences of 0.37 and 0.32, respectively). This study emphasizes the importance of recognizing variations in yoga practices and highlights the need for customization to enhance its integration into healthcare. To address the challenges posed by global diversity and practice heterogeneity, the study suggests moving from "black box" evaluations of yoga interventions toward data-driven analysis of macro- and micro-level factors. These insights can guide developers, healthcare providers, and researchers in creating culturally sensitive, user-friendly solutions and inform future cross-cultural research. Further related research will help create robust standards for yoga practices and delivery, applying the same rigor as that applied to conventional healthcare practices.

Gender Disparities in Workplace Stress Levels Among Physicians and Nurses

Work stress is increasingly recognized as a critical health concern that negatively impacts employee satisfaction and productivity, leading to elevated absenteeism and turnover rates. Studies suggest that women may face higher levels of work-related stress than men, although some research has not identified significant differences. The experiences of stress in the workplace differ between doctors and nurses, given the higher levels of interpersonal interaction in these roles. This cross-sectional observational study involved 200 doctors and 200 nurses, ensuring balanced gender representation. Demographic information was collected through a semi-structured proforma, and workplace stress was assessed using the Workplace Stress Scale. The results indicated that 66.2% of participants viewed their work environment as stressful, with a mean score of 19.01 on the Workplace Stress Scale. A notably higher proportion of females (72.5%) reported experiencing stress at work compared to males (60.5%, p&lt;0.05). Among the nursing staff, female nurses reported the highest stress levels (76%, mean score of 21.3), followed by female doctors (69%, mean score of 19.4), while male nurses (61%, mean score of 18.1) and male doctors (60%, mean score of 17.2) reported lower levels. These findings suggest that women perceive their work environments as significantly more stressful than their male counterparts. It is crucial to focus on enhancing working conditions for doctors and nurses, especially for female employees, as this can lead to improved outcomes for patients and the wider community. Keywords: Doctors, Gender Disparities, Nursing Staff, Work Stress.

Exploring the Discursive Representation of Pakistani Female Politicians in Print and Electronic Media: A Qualitative Study of Narrative and Framing

This study uses a qualitative discourse analysis approach to investigate Pakistani female politicians' discursive representation in print and electronic media. Specifically, the research focuses on the narratives and framing strategies employed by the media to construct and portray the public image of these political figures. By employing Corpus-Assisted Diachronic Discourse Analysis (CADDA), the study traces the changes in media discourse over time, uncovering how gender biases, societal norms, and power dynamics shape the representation of women in Pakistani politics. The findings reveal significant trends in gendered framing, highlighting how media portrayal fluctuates between marginalization and recognition of female politicians' contributions, though gender stereotypes remain deeply embedded in these narratives. The findings of this study have important implications for future media reform initiatives, advocating for a more equitable and balanced representation of women in politics. The insights gained can also inform gender advocacy strategies and policy efforts to enhance women's visibility and leadership roles in traditional and digital media.

Denoising for balanced representation: A diffusion-informed approach to causal effect estimation

Estimating individual treatment effects with observational data is crucial in causal inference. While it faces two major challenges: the absence of counterfactual outcomes and selection bias due to non-random assignment and intervention of confounding variables. This paper specifically addresses these by quantifying the distribution shift between the treated and control groups from a generative view to achieve balanced representation and then predict causal effects. We propose a novel method called Denoising for a Balanced Representation in Treatment Effect Estimation (DBRT). Motivated by the aim to generate analogous distributions in the Diffusion model, which aligns with our goal of achieving similar distributions in the latent space, our approach considers the divergence between the treated and control groups as’noise’ that needs to be removed incrementally. We then construct a network to fit and then denoise it step by step, ensuring a consistent reduction of discrepancies between the two groups. Additionally, to enhance the representation of input features, we incorporate memory vectors into the attention mechanism to capture prior information and combined correlations between covariates. Furthermore, we utilize the Hilbert–Schmidt Independence Criterion (HSIC) within the loss function to constrain the learned representation, ensuring its relevance to the original data. DBRT has demonstrated superior performance on different datasets compared to previous classic works, highlighting its effectiveness in treatment effect estimation.

Public accounting’s gender ledger: Where should audit firms target their gender initiatives to better balance representation?

Although men and women enter the public accounting profession at similar rates, women are not equally represented at the highest leadership levels. One possible contributor to the gender gap is that women and men enter the profession with differing levels of fit with the profession, which is the congruence between what an employee values and what an organization values. Using survey evidence from future public accounting professionals, we find that, on average, women experience lower fit with the public accounting profession, as compared to men, in three distinct areas: Women desire the public accounting profession to be more people-oriented, less demanding, and less competitive than they perceive it to be. Based on these results, we provide suggestions for firms to implement to improve fit for women in these areas. Our findings and suggestions will allow firms to develop improved gender initiatives that can assist in reducing gender disparity in the public accounting industry.

Anti-symmetric framework for balanced learning of protein-protein interactions.

Protein-protein interactions (PPIs) are essential for the regulation and facilitation of virtually all biological processes. Computational tools, particularly those based on deep learning, are preferred for the efficient prediction of PPIs. Despite recent progress, two challenges remain unresolved: (i) the imbalanced nature of PPI characteristics is often ignored and (ii) there exists a high computational cost associated with capturing long-range dependencies within protein data, typically exhibiting quadratic complexity relative to the length of the protein sequence. Here, we propose an anti-symmetric graph learning model, BaPPI, for the balanced prediction of PPIs and extrapolation of the involved patterns in PPI network. In BaPPI, the contextualized information of protein data is efficiently handled by an attention-free mechanism formed by recurrent convolution operator. The anti-symmetric graph convolutional network is employed to model the uneven distribution within PPI networks, aiming to learn a more robust and balanced representation of the relationships between proteins. Ultimately, the model is updated using asymmetric loss. The experimental results on classical baseline datasets demonstrate that BaPPI outperforms four state-of-the-art PPI prediction methods. In terms of Micro-F1, BaPPI exceeds the second-best method by 6.5% on SHS27K and 5.3% on SHS148K. Further analysis of the generalization ability and patterns of predicted PPIs also demonstrates our model's generalizability and robustness to the imbalanced nature of PPI datasets. The source code of this work is publicly available at https://github.com/ttan6729/BaPPI.

Assessing student readiness for mobile learning from a cybersecurity perspective

This research aims to measure student readiness in implementing mobile learning from a cybersecurity perspective. Using a quantitative method with a survey approach, the participants in this research were 150 students of the electrical engineering study program at Padang State University in Indonesia, who were randomly stratified to ensure a balanced representation of the academic year. The research is an online objective test related to cybersecurity topics. The data analysis technique used is quantitative descriptive. The analysis results show that system updates are the only indicator with a “good” awareness level, while other indicators are at the “sufficient” and “poor” levels, indicating the need for further improvement. These findings underscore the importance of integrating cyber security education in mobile learning curricula to increase student readiness to face evolving cyber threats. Thus, this research recommends developing specific training programs and adding comprehensive cybersecurity material to the curriculum to equip students with the skills necessary to maintain cybersecurity effectively.

A Survey of Perspectives and Educational Needs of Canadian Oncology Residents on Artificial Intelligence.

This study evaluated the perspectives and educational needs of Canadian oncology residents with regard to artificial intelligence (AI) in medicine, exploring the influence of factors such as program of choice, gender, and tech literacy on their attitudes towards AI. An ethics-approved survey collected anonymous responses from Canadian oncology residents from December 2022 to July 2023. Comparisons by demographics were made using Chi-square and Mann-Whitney U tests. A total of 57 residents and fellows responded out of an expected 182, with representation from each oncology training program in Canada. Over half of the participants were male (63.2%), with radiation oncology programs being better represented than medical oncology programs (68.4% vs. 31.6%). There was balanced representation across all years of training. Most trainees (73%) were interested in learning more about AI, and many believed the topic should be formally taught during residency (63%), preferably through workshops (79%). Among evaluated factors, tech literacy showed the most impact over AI perspectives, driving a perception shift towards viewing AI as an improvement tool, rather than as a threat to professionals. In conclusion, Canadian oncology residents anticipate AI's growing influence in medicine but face educational deficiencies. Gender, oncology discipline, and self-reported tech literacy impact attitudes toward AI, highlighting the need for inclusive education.

Exploring the Relationship Between Examination-Induced Anxiety and Cardiovascular Responses Among PT Students

This study explores the relationship between examination-induced anxiety and cardiovascular responses among physical therapy (PT) students. A quasi-experimental repeated measures design was utilized to examine heart rate (HR), blood pressure (BP), and heart rate variability (HRV) at three stages: before, during, and after high-stakes Objective Structured Clinical Examinations (OSCEs). A total of 120 PT students (60 male, 60 female) were recruited using purposive sampling, with data collection extended over two academic years to ensure balanced gender representation. Anxiety was measured using the State-Trait Anxiety Inventory (STAI), while cardiovascular responses were continuously monitored using wearable devices. Results revealed significant increases in HR and BP, alongside decreases in HRV during the examination, reflecting heightened autonomic stress. Post-exam, cardiovascular markers demonstrated partial recovery but remained elevated compared to pre-exam levels, indicating lingering physiological effects of anxiety. Qualitative data from student interviews revealed cognitive impairments and lingering anxiety post-exam, corroborating the physiological findings. Repeated measures ANOVA indicated significant changes in cardiovascular responses over time, with no significant interaction effects for gender. These findings underscore the impact of examination-induced anxiety on both physiological and psychological outcomes in PT students. The persistent elevation in cardiovascular markers post-exam suggests that students may not fully recover from stress immediately, highlighting the need for targeted interventions, such as mindfulness or biofeedback, to help manage anxiety. Addressing examination stress could enhance both student well-being and academic performance, mitigating the long-term risks associated with chronic stress.

The Water Balance Representation in Urban‐PLUMBER Land Surface Models

AbstractUrban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance, which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban‐PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators expecting better scoring models to capture the latent heat flux more accurately. We find that the water budget is only closed in 57% of the model‐site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all water balance indicators for any site. Models passing more indicators do not capture the latent heat flux more accurately refuting our hypothesis. While output reporting inconsistencies may have negatively affected model performance, our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

Different Colours of the same culture: effects of father support programs on father involvement

ABSTRACT The aim of the study is to analyze the cross-cultural comparison of fathers’ involvement who participated and did not participate in father support programs. To reach that aim, the father’s participation in self-care and daily life issues, participation in leisure-time activities, participation in school-related issues, discipline, and parental relationships were investigated. The sample consisted of totally 20 fathers (10 Azerbaijani and 10 Turkish fathers), providing a balanced representation from both cultural backgrounds. Ten fathers (5 fathers from Turkey and 5 fathers from Azerbaijan) had actively attended the programs, while the remaining ten fathers had not participated in any formal father support programs. Data of the study were collected from fathers through an online form via considering voluntary and easy accessibility principles. The study contributes to comprehending the impact of father support programs on fathers’ involvement and highlights cultural differences in fathering practices. The findings of the study were discussed considering the relevant literature and suggestions were offered.