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Psychology and Research Assessment in the United Kingdom
Get PDFWhat can we learn about psychology research in the UK, and its perceived quality, from examining manuscripts submitted to the psychology, psychiatry and neuroscience subpanel of the 2021 Research Excellence Framework (REF2021)? Using a latent Dirichlet allocation topic modelling approach, we identified 33 topics which collectively summarised the content of the journal articles returned to the subpanel. We found that the composition of submissions to the subpanel, in terms of these topics, explained a large proportion of the variance in the quality assessments they received from the expert peer review subpanel. Our model identified topics which were typically associated with receiving higher and lower unit-level quality assessments. In our discussion we pay particular attention to the fate of qualitative research, and discuss possible accounts for why units who returned a large amount of qualitative work tended to receive lower quality assessments than those who did not
Integration and innovation: medical and health consortia improving continuing medical education in China
Get PDFBackground: Primary health care (PHC) is the cornerstone of the healthcare system in China. The medical and health consortia (medical consortia) integrate resources of continuing medical education (CME) to bridge competency gaps among healthcare providers. This narrative review aims to explore the innovative models of CME within the framework of medical consortia. Methods: Searches were conducted in both Chinese and English databases to broaden the scope of the review, including China National Knowledge Infrastructure, Wanfang Data, and PubMed. Chinese policy documents were retrieved from official websites of China’s National Health Commission. The review analyzed existing policy documents (2010–2025) and relevant literature, supplemented by an institutional application example of the West China Hospital–Fangcao Community Health Service Center Medical Consortium to explore challenges and recommendations. Results: China developed a series of policies to promote the construction of medical consortia, with a focus on resource-sharing between tertiary and PHC institutions. A literature search yielded 196 articles, including qualitative studies, quantitative studies, and reviews, of which 48 met inclusion criteria in the review. Seven policy documents were included in the analysis. The synergy between medical consortia and CME brought benefits to both healthcare providers and the health system. Key innovations included clinical scenario-oriented training, remote consultation, and flexible training modalities. However, the reviewed literature highlighted persistent challenges, including regional disparities in resources, limited financial incentives for general practitioners (GPs), and a shortage of qualified trainers. Overcoming barriers such as regional resource disparities and improving the intrinsic motivation of GPs remained critical to the implementation of CME. Conclusion: Medical consortia offer platforms for the delivery of CME, while CME supports the development of medical consortia. These innovations enhance collaboration between specialists and GPs, thereby optimizing patient referrals and follow-up care
Perinatal mental health: the role of social inequalities and domestic abuse on maternal outcomes
Get PDFPerinatal mental health conditions (depression, anxiety disorders, and postpartum psychosis/bi-polar disorder) affect one in four new/expectant mothers. We assessed the impact of social inequalities, sociodemographic factors, domestic abuse and maternal interactions with social services in childhood on mortality and morbidity outcomes in women with these conditions in Northern Ireland (NI). Methods We identified a population-based cohort of 179,723 mothers giving birth in NI between 1st January 2010 and 31st December 2022 (301,110 pregnancies). Eight datasets were linked: maternity, death records, prescriptions, hospital episodes/admission/discharges, A&E, mental health inpatient data, social services, and the rateable value of the property of maternal residence during pregnancy. Sociodemographic variables included maternal age, parity, gestational age, single parent family, marital status, employment status, and ethnic group. Domestic abuse was identified from the maternity disclosure variables and A&E data. Maternal morbidity outcomes included mental health medication use and hospital admissions. Results We will present the:•Incidence and prevalence of perinatal mental health conditions occurring during pregnancy and in the first year following birth.•Sociodemographic and socioeconomic determinants of perinatal mental health conditions and the impact of these on maternal mortality and morbidity outcomes in women with perinatal mental health conditions.•Incidence and impact of domestic abuse (and associated sociodemographic and socioeconomic risk factors) on maternal mortality and morbidity outcomes in women with perinatal mental health conditions.•Relationship between maternal interactions with social services in childhood and maternal mortality and morbidity outcomes in women with perinatal mental health conditions. •How multiple adversity (socioeconomic inequalities, socio-demographic factors, maternal interactions with social services in childhood, and domestic abuse) contributes to maternal mortality and morbidity outcomes in these women. Conclusion This is the first population-wide study to investigate how multiple adversity affects mortality and morbidity outcomes in women with perinatal mental health conditions. The impact of this research will enable early effective support and interventions to be implemented to minimise adverse outcomes in this vulnerable population. <br/
The structure of mass political belief systems: A network approach to understanding the left-right spectrum
Get PDFMany socially consequential beliefs, notably political and religious ideologies, consist not of single propositions in isolation from others but as systems of many propositions. Philip Converse, one of the most influential political scientists of the twentieth century, proposed that such systems can be understood as networks of propositions and predicted that they would be highly intercorrelated in those with strong ideological commitments but less so in people who are less ideological. We used recent advances in network psychometrics to test this account in relation to the political beliefs of a representative sample of 2,058 UK adults, who rated themselves on the left-right dimension and then reported their attitudes toward 18 policy issues. We divided participants into equally-sized groups of left-wing, centrist and right-wing participants and found that, as Converse had predicted, the networks of those at either end of the left-right continuum were similar in structure, being significantly more interconnected than the networks of those who identified themselves as centrists, even though the actual beliefs were (for the most part) polar opposites. This finding, which was robust to sensitivity checks, aligns with previous research which has shown that people at the political extremes, compared to those in the centre, are more certain about their beliefs and less likely to change them over time. In each ideological group we also identified the same three communities of beliefs which mapped onto classic accounts of authoritarian attitudes, altruism and cooperativeness, and personal liberty. Attitudes towards gay rights had the highest predictability index in all three networks and was the most central node in the right and centre networks, suggesting that these attitudes play a largely unrecognised but important role in ideological positioning. Our analytical approach has implications for not only political beliefs but all organized belief systems
Formulation and Systematic Optimisation of Polymeric Blend Nanoparticles via Box–Behnken Design
Get PDFBackground/Objectives: Despite the advantages of polycaprolactone (PCL) for drug delivery, it still lacks effective approaches to enhance its encapsulation of drugs. Blending PCL with less hydrophobic polymers can tailor physicochemical properties to overcome these limitations. This study, for the first time, integrates two beneficial approaches—polymer blending and Box–Behnken design (BBD) optimisation—to develop PCL-based blend nanoparticles (NPs) with enhanced encapsulation efficiency (EE), controlled particle size, and improved stability through surface charge modulation. Methods: Drug-loaded blend NPs were developed using a double emulsion method, with different polymer ratios. A BBD model was employed to investigate the influential factors that control the size, charge, and EE. Results: Blending PCL with a less hydrophobic polymer significantly improved EE, achieving 60.96% under optimal conditions. The BBD model successfully predicted conditions for obtaining NPs with optimum size, negative charge, and enhanced drug encapsulation. The drug amount was identified as the most influential factor for EE, while polymer amounts significantly impacted size and charge. Conclusions: Careful control of polymer ratios, drug amount, and surfactant levels was shown to significantly influence particle size, surface charge, and EE, with the balanced 50:50 PCL:PLGA blend achieving optimal physicochemical performance. Using the BBD, the study identified the predicted optimal formulation consisting of 162 mg polymer blend, 8.37 mg drug, and 8% surfactant, which is expected to yield NPs with a size of 283.06 nm, zeta potential of −31.54 mV, and EE of 70%. The application of BBD allowed systematic evaluation of the factors and their interactions, providing robust predictive models
Energy-Efficient and Attacks Resilient PUF design Exploiting VGSOT-MTJ
Get PDFSpintronic Physically Unclonable Functions (PUFs) show promise in enhancing electronic system security due to their inherent randomness, low energy consumption, fast response times, and temperature stability. This paper presents a novel PUF based on voltage-gated spin-orbit torque magnetic tunnel junctions (VGSOT-MTJs) that compares the resistance of MTJ cells utilizing intrinsic process variations to get an output response. Compared to arbiter PUFs, the proposed PUF provides a significantly larger effective challenge-response pair (CRP) space by supporting multiple independent configurations and is also reconfigurable. The Proposed VGSOT-MTJ based PUF implemented at 45 nm technology achieves a lower energy consumption of 63.67 fJ/bit and a throughput of 0.27 Gb/s at a supply voltage of 1 V. The proposed PUF achieves near-ideal uniqueness of 50.2% and a high reliability of 97.3%. Moreover, the proposed PUF demonstrates strong resistance to both machine learning (ML) and side-channel attacks. An MLattack using a multilayer perceptron (MLP) yielded a prediction accuracy of under 55.27%, indicating the PUF's resilience. The correlation power analysis (CPA) confirmed the PUF's robustness against side channel attacks. The designed VGSOT-MTJ based PUF shows robust performance with higher energy efficiency and is highly suitable for resource constrained internet of things applications
Artificial Intelligence-Assisted Image Extraction in Neonatal Echocardiography for Congenital Heart Disease Diagnosis in Sub-Saharan Africa: Protocol for Model Development
Get PDFBackground:Sub-Saharan Africa (SSA) bears the highest global burden of under-5 mortality, with congenital heart disease (CHD) as a major contributor. Despite advancements in high-income countries, CHD-related mortality in SSA remains largely unchanged due to limited diagnostic capacity and centralized health care. While pulse oximetry aids early detection, confirmation typically relies on echocardiography, a procedure constrained by a shortage of specialized personnel. Artificial intelligence (AI) offers a promising solution to bridge this diagnostic gap.Objective:This study aims to develop an AI-assisted echocardiography system that enables nonexpert operators, such as nurses, midwives, and medical doctors, to perform basic cardiac ultrasound sweeps on neonates suspected of CHD and extract accurate cardiac images for remote interpretation by a pediatric cardiologist.Methods:The study will use a 2-phase approach to develop a deep learning model for real-time cardiac view detection in neonatal echocardiography, utilizing data from St. Padre Pio Hospital in Cameroon and the Red Cross War Memorial Children’s Hospital in South Africa to ensure demographic diversity. In phase 1, the model will be pretrained on retrospective data from nearly 500 neonates (0-28 days old). Phase 2 will fine-tune the model using prospective data from 1000 neonates, which include background elements absent in the retrospective dataset, enabling adaptation to local clinical environments. The datasets will consist of short and continuous echocardiographic video clips covering 10 standard cardiac views, as defined by the American Society of Echocardiography. The model architecture will leverage convolutional neural networks and convolutional long short-term memory layers, inspired by the interleaved visual memory framework, which integrates fast and slow feature extractors via a shared temporal memory mechanism. Video preprocessing, annotation with predefined cardiac view codes using Labelbox, and training with TensorFlow and PyTorch will be performed. Reinforcement learning will guide the dynamic use of feature extractors during training. Iterative refinement, informed by clinical input, will ensure that the model effectively distinguishes correct from incorrect views in real time, enhancing its usability in resource-limited settings.Results:Retrospective data collection for the project began in September 2024, and to date, data from 308 babies have been collected and labeled. In parallel, the initial model framework has been developed and training initiated using a subset of the labeled data. The project is currently in the intensive execution phase, with all objectives progressing in parallel and final results expected within 10 months.Conclusions:The AI-assisted echocardiography model developed in this project holds promise for improving early CHD diagnosis and care in SSA and other low-resource settings.International Registered Report Identifier (IRRID):DERR1-10.2196/75270JMIR Res Protoc 2025;14:e7527
Home working during the COVID-19 pandemic: The experience of drug and alcohol support workers
Get PDFBackground: Drug and alcohol support workers play a vital role in addressing the growing burden of substance-related harm and mortality. The COVID-19 pandemic led to an abrupt and significant shift towards home working for many in this workforce. This study explores these workers’ home working experiences, addressing a research gap and providing valuable insights for staff, organisations delivering public health services, and service users. Design and methods: This qualitative study explores home working experiences of 30 drug and alcohol support workers in northern England during the COVID-19 pandemic. Data collection included innovative digital methods: (1) digital timelines (n = 16); (2) in-depth interviews (n = 17); (3) five focus groups (n = 12). Timeline text was treated as qualitative text data. Interviews and focus groups were recorded, transcribed, and coded. Data were subject to Framework Analysis. Results: Seven themes were identified: (1) Difficulty balancing and separating work and home life; (2) Importance of setup, infrastructure and conducive work environment; (3) The move to remote/home working – a process; (4) Convenience and efficiency benefits; (5) Loss of the social: – reductions in social connectedness and feelings of isolation; (6) The importance of the ‘office’ for connection, communication, socialising, and information sharing; (7) Managing remotely – the development and implementation of strategies and ways of coping. Conclusions: While home working offers some benefits for substance use support workers, providers, and service users, it also introduces significant challenges. Understanding these is critical for service optimisation. A hybrid (in-person/remote) deliver model, combining home and co-located, office-based working may be optimal.</p