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Exploring the well-being of early career teachers: staying afloat whilst fixing the boat during COVID-19
Get PDFThe COVID-19 pandemic had a major impact on schools, leading to far-reaching and rapid responses by those in the sector. This included the cohort of student-teachers who were training to teach during the 2019–2020 academic year. Due to a national UK lockdown in the spring and summer of 2020, this cohort of individuals were unable to undertake school placements where they would gain the majority of their classroom experience before starting as qualified teachers. This paper reports on data we collected in a British Academy funded study from members of the cohort as they started their first year of teaching, to try to understand the impact of the loss of practical classroom teaching whilst training and to understand the extent of the impact this had on their well-being as they entered an unfamiliar and stressed sector. The results from the analysis suggest that this cohort of newly qualified teachers were meeting multiple challenges, which in some cases had a reported impact on their well-being. However, where they were well supported, and where strong professional relationships were developed, and performative measures were reconsidered, these challenges were more than compensated for by the resources individuals could draw on to ensure their continued development and positive well-being. However, there are still questions to answer as to how this cohort will react as schools return to performative, accountability-driven contexts, approaches to education that this cohort have had little experience of
Perfectionism, Wellbeing, and Coping Among Filipino University Students: A Multi-Study Test of the 2 × 2 Model of Perfectionism
No full textBackground: Perfectionism is an important characteristic among university students given its associations with their wellbeing and coping. One approach to studying student perfectionism is Gaudreau and Thompson’s (2010) 2 × 2 model of perfectionism, which examines the interaction between self-oriented perfectionism and socially prescribed perfectionism (SPP). The model is useful for studying student perfectionism, but tests in different cultural contexts remain limited, with some suggesting its hypotheses need modification.
Objectives: This article builds on existing research by presenting two novel studies that provide the model’s first tests in predicting university student wellbeing and coping in a Filipino context, as well as tests of alternate cultural makeup and aggravating factor hypotheses for SPP’s role.
Methods: Following preregistered protocols, two independent samples of Filipino university students completed questionnaires measuring variables of interest – one cross-sectionally (N = 294) and one longitudinally (N = 324) with a 3-month follow-up.
Results and Conclusion: Moderated regression analyses showed support for the model’s hypotheses across both samples depending on the variable. Findings provided clearer support that students with high SPP or a strong belief that others expect perfection are more vulnerable to poorer wellbeing and unhealthy coping, making SPP an aggravating factor in the Filipino context
Stacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features
Get PDFBackground: Parkinson’s disease (PD) is a progressive neurodegenerative condition that impairs motor and non-motor functions. Early and accurate diagnosis is critical for effective management and care. Leveraging machine learning (ML) techniques, this study aimed to develop a robust prediction system for PD using a stacked ensemble learning approach, addressing challenges such as imbalanced datasets and feature optimization. Methods: An open-access PD dataset comprising 22 vocal attributes and 195 instances from 31 subjects was utilized. To prevent data leakage, subjects were divided into training (22 subjects) and testing (9 subjects) groups, ensuring no subject appeared in both sets. Preprocessing included data cleaning and normalization via min–max scaling. The synthetic minority oversampling technique (SMOTE) was applied exclusively to the training set to address class imbalance. Feature selection techniques—forward search, gain ratio, and Kruskal–Wallis test—were employed using subject-wise cross-validation to identify significant attributes. The developed system combined support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN), and decision tree (DT) as base classifiers, with logistic regression (LR) as the meta-classifier in a stacked ensemble learning framework. Performance was evaluated using both recording-wise and subject-wise metrics to ensure clinical relevance. Results: The stacked ensemble learning model achieved realistic performance with a recording-wise accuracy of 84.7% and subject-wise accuracy of 77.8% on completely unseen subjects, outperforming individual classifiers including KNN (81.4%), RF (79.7%), and SVM (76.3%). Cross-validation within the training set showed 89.2% accuracy, with the performance difference highlighting the importance of proper validation methodology. Feature selection results showed that using the top 10 features ranked by gain ratio provided optimal balance between performance and clinical interpretability. The system’s methodological robustness was validated through rigorous subject-wise evaluation, demonstrating the critical impact of validation methodology on reported performance. Conclusions: By implementing subject-wise validation and preventing data leakage, this study demonstrates that proper validation yields substantially different (and more realistic) results compared to flawed recording-wise approaches. The findings underscore the critical importance of validation methodology in healthcare ML applications and provide a template for methodologically sound PD classification research. Future research should focus on validating the model with larger, multi-center datasets and implementing standardized validation protocols to enhance clinical applicability
Multiple Impacts on Adolescent Well-Being During COVID-19 School Closures: Insights From Professionals for Future Policy Using a Conceptual Framework
Get PDFPurpose
This study explores the impacts of school closures during the COVID-19 pandemic on the domains of adolescent well-being from the UN H6+ framework, reported by health and educational professionals worldwide.
Methods
Semistructured individual online interviews were conducted in six languages during the second wave of the COVID-19 pandemic (March–December 2021) with health and education professionals who volunteered for follow-up after participating in an anonymous online survey. The UN H6+ 5-domain conceptual framework of adolescent well-being was used as a framework for the directed content analysis of the combined interview dataset.
Results
A total of 60 interviews—translated into English—were analyzed from 38 education and 22 health professionals in 28 countries/territories. Participant reports showed impacts on all five adolescent well-being domains, but mainly domain 1 (good health and nutrition), domain 3 (safety and a supportive environment) and domain 4 (learning, competence, education, skills, and employability). Reflections of 2-connectedness and 5-agency were also present. Their reports included mainly negative impacts, but also some positive insights to take forward.
Discussion
Policymakers must recognize impacts of school closures during the pandemic on multiple domains of adolescent well-being and the potential for widening inequalities. Schools play a critical mitigating role that goes beyond education. The call to action for the adolescent health community is to recognize and address ongoing potential long-term impacts on well-being and inequalities in their everyday practice. It is also important to advocate locally, nationally, and globally for careful consideration of the consequences of school closures in future health crises
A case of ruptured infrapatellar bursa sac with Baker's cyst
Get PDFA 40-year-old female arrived with persistent posterior right knee pain, swelling in the popliteal, infrapatellar, anterior calf areas and difficulty walking due to joint stiffness. Multiple hypoechoic collections with internal echoes and debris were discovered in the anterior calf region using ultrasound imaging, which extended from a thick-walled infrapatellar hypoechoic collection with peripheral vascularity. A significant popliteal fossa cyst of comparable appearance was also observed. The results were consistent with a ruptured infrapatellar bursa sac and a popliteal fossa (Baker's cyst). The patient received conservative treatment with anti-inflammatory drugs, leading to the resolution of symptoms after a period of 6 weeks. For patients with more complex cysts, procedures like aspiration and corticosteroid injections under ultrasound guidance may be necessary. Recognizing the condition and using ultrasound for diagnosis and management can lead to successful outcomes
Evaluating AI adoption in healthcare: Insights from the information governance professionals in the United Kingdom
Get PDFBackground
Artificial Intelligence (AI) is increasingly being integrated into healthcare to improve diagnostics, treatment planning, and operational efficiency. However, its adoption raises significant concerns related to data privacy, ethical integrity, and regulatory compliance. While much of the existing literature focuses on the clinical applications of AI, limited attention has been given to the perspectives of Information Governance (IG) professionals, who play a critical role in ensuring responsible and compliant AI implementation within healthcare systems.
Objective
This study aims to explore the perceptions of IG professionals in Kent, United Kingdom, on the use of AI in healthcare delivery and research, with a focus on data governance, ethical considerations, and regulatory implications.
Methods
A qualitative exploratory design was employed. Six IG professionals from NHS trusts in Kent were purposively selected based on their roles in compliance, data governance, and policy enforcement. Semi-structured interviews were conducted and thematically analysed using NVivo software, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT).
Results
Thematic analysis revealed varying levels of AI knowledge among IG professionals. While participants acknowledged AI’s potential to improve efficiency, they raised concerns about data accuracy, algorithmic bias, cybersecurity risks, and unclear regulatory frameworks. Participants also highlighted the importance of ethical implementation and the need for national oversight.
Conclusion
AI offers promising opportunities in healthcare, but its adoption must be underpinned by robust governance structures. Enhancing AI literacy among IG teams and establishing clearer regulatory frameworks will be key to safe and ethical implementation
Increasing activity and reducing sedentary behaviour for people with severe mental illness: what are the active ingredients for behaviour change? A systematic review.
No full textIncreasing physical activity (PA) and reducing sedentary behaviour (SB) can improve health outcomes and reduce rates of premature mortality for people with severe mental illness (SMI). In this systematic review we aimed to explore the active ingredients of existing PA interventions for people with SMI. We reviewed intervention functions, behaviour change techniques (BCTs), contextual features and underpinning theories. We included 15 PA interventions, of which 4 were classed as effective (effect size >0.273). We identified the frequency of intervention functions and BCTs that were used in each study and compared the number of effective studies that featured a particular BCT or intervention function with the total number that featured those components. We used the TIDieR checklist to document contextual features that might be important within effective interventions including the theories that guided the development of interventions. The most frequently used functions were education and environmental restructuring, both of which were identified in effective interventions. The BCTs that were identified as potentially useful were framing and reframing, feedback on behaviour and self-monitoring. No discernible contextual features were unique to the effective interventions, but combinations of some features seemed to be (PA tracking, educational components and support delivered by community health teams). More high quality and better reported studies are required to strengthen this evidence base.Prospero registration: PROSPERO 2024 CRD4202454185
Illiberal Democracy and the Erosion of Academic Freedom in the ‘New’ Türkiye
No full textIn the aftermath of 9/11, Türkiye’s democratic path has been questioned due to the debates on Islam’s compatibility with Western liberal democracy. The central puzzle of this article is to analyse how Türkiye’s initial trajectory of democratization took an illiberal turn with the erosion of academic freedom under the Justice and Development Party (AKP), deviating from the expectations of a transition from electoral to liberal democracy. After establishing the conceptual and theoretical framework in the first part, the next one summarizes the Turkish paradoxical engagement with liberal democracy and military coups throughout the twentieth century and their impact on academic freedom. The third part focuses on democratic backsliding and a silent regime change under the AKP rule. The last one analyses the symbiotic relationship between the attacks on academic freedom and ‘the rise of illiberal democracy’ in the ‘new’ Türkiye. The article concludes by arguing that the erosion of academic freedom reflects Türkiye’s deepening democratic backsliding and increasing culture of fear and self-censorship during the consolidation of illiberal democracy
IN VITRO EVALUATION OF CYTOGENETIC DAMAGE BY GRAPHENE OXIDE (15-20 SHEETS) NANOMATERIALS IN HUMAN BLOOD LEUKOCYTES FROM HEALTHY INDIVIDUALS AND PULMONARY DISEASE PATIENTS DIAGNOSED WITH ASTHMA, COPD AND LUNG CANCER
Get PDFFor the past few decades, the use of graphene oxide (GO) nanomaterials (NMs) has increased exceedingly due to
their biomedical applications in the drug delivery of anti-cancer drugs. Their unique physicochemical properties
and good surface chemistry with unbound surface functional groups enable covalent bonding with organic
molecules such as RNA and DNA, making GO NMs excellent candidates for drug delivery nanocarriers. Despite
the increased use in biomedical applications, there are concerns about their genotoxicity. Only a few studies on
GO NMs’ impact on DNA have been published on humans, let alone on patients diagnosed with chronic
pulmonary diseases. This study investigates for the first time the effects of commercial GO (15-20 sheets; 4-10%
edge-oxidized; 1 mg/ml) in vitro, in particular the DNA damage but also other genotoxic endpoints in whole blood
and peripheral blood leucocytes (PBL) from healthy individuals and patients diagnosed with chronic pulmonary
diseases, i.e., asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. After detailed
characterization of commercial GO NMs, cytotoxicity studies were conducted using the dimethyl thiazolyl
diphenyltetrazolium bromide (MTT) and neutral red uptake (NRU) assays. In contrast, genotoxicity (DNA
damage and chromosome aberration parameters) was studied using alkaline Comet and cytokinesis-blocked
micronucleus (CBMN) assays. Our results showed concentration-dependent increases in cytotoxicity,
genotoxicity, and chromosome aberrations, with PBL from COPD and lung cancer patients being more sensitive
to DNA damage compared with asthma patients and healthy control individuals. GO NMs may have promising
roles in drug delivery applications when formulated to deliver drug payloads to cells for treating COPD or cancer
cells. But the fact that cytotoxicity, genotoxicity, and chromosome instability parameters as biomarkers of cancer
risk were increased in exposed cells from healthy individuals should be of concern regarding public health,
especially in occupational exposures and in medical treatments when using GO NMs as drug delivery nano-
carriers
A Hybrid Ensemble of Denoising Autoencoders and Deep Learning Models for Fetal Image Analysis
Get PDFMedical image analysis, particularly ultrasonography, has involved increasing attention in computer science and engineering due to its potential for automated and scalable interpretation. Ultrasound imaging is widely used in prenatal care because of its non-invasive nature and cost-effectiveness. Automated analysis of fetal ultrasound images can improve diagnostic accuracy and reduce inter-observer variability. However, challenges such as speckle noise, low contrast, and anatomical variations across trimesters make automated interpretation difficult, requiring robust preprocessing, segmentation, and classification methods.
This study proposes a hybrid ensemble deep learning framework for analyzing fetal ultrasound images. The framework integrates a denoising autoencoder for noise reduction and image enhancement, as well as seven segmentation architectures (U-Net, DeepLabV3+, DenseNet-U-Net, MFP-UNet, Attention U-Net, MobileNet-U-Net, and ResNet-U-Net), and five ensemble strategies (maximum voting, majority voting, weighted voting, confidence-based fusion, and averaging) to enhance segmentation performance. A multi-input classification approach is also introduced, combining individual and ensemble segmentation outputs in a fine-tuned DenseNet121 for trimester categorization (first, second, and third trimesters) based on head circumference and femur length.
The framework is evaluated using Dice score, mean intersection over union, accuracy, precision, recall, and F1-score. Experimental results show that ensemble strategies significantly improve segmentation. The multi-input classification achieves 92.50% accuracy for head circumference and 90.60% for femur length on the custom dataset, as well as 83.68% on the HC18 dataset, outperforming individual models.
The main contributions include (1) a hybrid ensemble strategy for robust segmentation and (2) a multi-input trimester classification method. The proposed framework is generalizable and can be extended to other medical imaging applications beyond fetal ultrasound analysis