Sheffield Hallam University

To investigate changes in the prevalence of chronic pain (CP), high-impact chronic pain (HICP), and musculoskeletal health (MSK-HQ scores) before and after the COVID-19 pandemic. Longitudinal cross-sectional study using data from two population-based surveys conducted in 2016/17 (PRELIM) and 2022/23 (MIDAS-POP). Adults aged 35 and over registered with general practices in the West Midlands, UK, completed questionnaires including CP, HICP, and MSK-HQ. Entropy balancing was applied to match respondents across survey years on age, sex, deprivation, and ethnicity. Logistic and linear regressions estimated odds ratios (ORs) and mean differences. Subgroup analyses tested for interactions, and E-values were calculated to assess robustness to unmeasured confounding. CP prevalence increased from 35.6% in 2016/17 to 42.9% in 2022/23 (OR 1.36, 95% CI 1.22 to 1.51), and HICP from 15.5% to 22.4% (OR 1.57, 1.38 to 1.80). MSK-HQ scores among people with CP declined from 32.4 to 23.1, exceeding the minimal important change. Increases were more marked among women and people aged 35-54. Differences by deprivation and ethnicity were observed, although subgroup estimates for ethnicity should be interpreted cautiously due to small sample sizes. E-values suggested that the associations were moderately robust to unmeasured confounding. Chronic pain prevalence and its burden have increased since the COVID-19 pandemic, alongside declines in musculoskeletal health. While some of this may reflect residual confounding, the findings underscore the need for targeted pain prevention and management strategies, particularly for working-age adults and underserved groups.

Autistic children and young people may be more likely than their peers to access healthcare services, but little is known about their experiences of healthcare as the literature tends to focus on the experiences of autistic adults and the views of healthcare professionals. The literature review detailed in this article aimed to gather evidence on the experiences of autistic children or young people and their parents in accessing healthcare services. Following a database search and screening, seven articles were selected for review. None of the studies captured children's experiences but the findings offer some insight into parents' views. Four main themes emerged from the data analysis: importance of the healthcare professional; relationship between families and staff; impact on parents; and healthcare services and environment. These themes encompass the need for: effective relationships between staff and parents to ensure the provision of family-centred care; further education among professionals in caring for autistic children and young people to enhance their experiences of healthcare; and greater flexibility in healthcare services to accommodate the needs of autistic children and young people and their families.

Abstract Management and organization research has paid limited attention to how gender, time and organizational dynamics intersect, particularly in shaping gender equality and inclusion. The extant literature also remains largely western‐centric in its focus on the conceptualizations of time and gendered time use. Thus, the present study examines the complex interaction between gender, time and organizations in a non‐western context, specifically in Africa. Through a systematic literature review of 88 articles, we provide insights into the gendered impact of time on inclusion in African organizations. In doing so, we make two key contributions to the literature. First, we develop a conceptual framework that identifies five distinct gendered conceptualizations of time, which offer a culturally grounded lens for analysing inclusion. Second, we highlight the main theoretical perspectives of gendered time use based on our reviewed literature. Furthermore, we also suggest directions for future research to advance research on gender, time and organizations.

Semiconductor lithography, a pivotal process in integrated circuit (IC) fabrication, accounts for approximately 30% of production costs and faces significant challenges as feature sizes shrink to sub-nanometer scales. Optical diffraction and process-induced distortions complicate precise patterning, necessitating advanced techniques beyond traditional Optical Proximity Correction (OPC). Inverse Lithography Technology (ILT) offers a mathematically robust approach to enhance pattern fidelity, yet its high computational complexity limits scalability. We propose Adaptive Reinforcement Learning for Lithography Optimization (ARLO), a U-Net-based framework integrating self-attention mechanisms and reinforcement learning (RL) to iteratively optimize photomasks using real-time lithographic simulations. Evaluated on the LithoBench benchmark, ARLO achieves a 37.8% reduction in [Formula: see text] Loss and a 74.0% reduction in Process Variation Band (PVB) compared to GAN-OPC, alongside 14.7% and 9.1% [Formula: see text] Loss reductions and 51.3% and 37.1% PVB reductions versus Deep LithoNet (DLN) and RL-ILT, respectively. Despite a higher shot count (181.4% increase vs. GAN-OPC, 59.0% vs. DLN-1, 29.4% vs. RL-ILT), ARLO maintains a competitive runtime of 0.035 seconds per patch. These results position ARLO as a scalable, efficient solution for next-generation semiconductor manufacturing.

The last two deglaciations mark transitions from glacial to interglacial climates, dramatically reshaping Northern Hemisphere ice sheets. Numerical modelling of these transitions provides critical insight into the processes controlling ice-sheet retreat and collapse. Comparing the last two deglaciations allows us to evaluate how different forcings and initial conditions influence ice-sheet dynamics and understand the interplay between orbital forcing, greenhouse gases, abrupt climate changes and ice sheet instabilities in driving ice sheet evolution.We use the fast yet comprehensive coupled General Circulation Atmosphere–ice-sheet model FAMOUS–BISICLES to simulate the Northern Hemisphere ice-sheet evolution during the penultimate deglaciation (140–128 thousand years ago; ka) and the last deglaciation (21-7 ka), with particular interest in the abrupt Bølling warming (14.5 ka). Our simulations follow the PMIP4 (Palaeoclimate Model Intercomparison Project 4) protocols and are forced with prescribed sea surface temperatures and sea ice from transient climate model outputs to reduce biases and force millennial abrupt climate changes.First, we compare the penultimate and last deglaciations to assess how orbital forcing, greenhouse gas concentrations, and uncertain model parameters and SST inputs shape both the pace and spatial patterns of ice retreat. Results indicate a faster ice retreat during the penultimate deglaciation. Sensitivity experiments show that the rate of deglaciation is particularly sensitive to processes that impact the surface mass balance, but ice dynamics also play an important role. Sub-shelf melt rate is less significant; however, it can be important where confined ice shelves are able to form. Although insolation drives the deglaciations, rising greenhouse gases and warming SSTs significantly amplify the ice-sheet response to orbital forcing.Second, we focus on the abrupt Bølling warming (~14.5 ka). Our simulations show accelerated deglaciation during this event, though the magnitude of response depends on the ice-sheet topography during the warming and on the pattern of abrupt SST increase prescribed. Marine-based sections, particularly the Barents–Kara ice sheet, exhibit the greatest sensitivity to prescribed ocean changes.

After half a century of grass-roots advocacy and academic research, coercive or controlling behaviour (CCB) is still not widely understood or recognised as the most pervasive form of intimate partner violence (IPV). Since the criminalisation of CCB, the police have been a key agency in victim support in the UK, yet effective response remains a work in progress. This qualitative study focuses on the cultural narratives which influence individual officers in their interactions with victim-survivors of CCB. Critical discourse analysis of in-depth interviews with officers reveals that socio-economic status is a significant factor in perception of IPV: in lower socio-economic households IPV is more likely to be interpreted as situational and episodic, while in higher socio-economic households it is more likely to be interpreted as a deliberate pattern of CCB. This finding suggests that cultural narratives and assumptions should be unpacked as part of officer education and development on CCB.

Purpose This study aims to address the challenges of complex contact dynamics, structural constraints and perceptual uncertainty in robotic peg-in-hole assembly tasks, particularly under large workspace and high-precision requirements. Design/methodology/approach The authors propose a reinforcement learning framework that integrates multimodal perception, self-supervised representation modeling and hybrid control mechanisms. The framework takes visual images, proprioceptive states and target pose information as inputs. A self-supervised pretraining phase jointly optimizes image reconstruction and forward prediction to learn structurally aware and temporally consistent latent state representations. Furthermore, a Spectrum Random Masking (SRM) technique introduces frequency-domain perturbations to the visual modality, encouraging stable spectral feature learning and enhancing perception robustness for sim-to-real transfer. During execution, an adaptive impedance control mode is activated when excessive contact force is detected, mitigating insertion impacts and jamming. Findings Real-world experiments in extended operational spaces and across diverse peg-hole geometries demonstrate that the proposed method achieves millimeter-level accuracy, high success rates in seen configurations and strong zero-shot generalization to unseen shapes. These results validate the effectiveness of the framework in ensuring robust and precise robotic assembly across large and variable workspaces. Originality/value This work presents a novel reinforcement learning framework that integrates multimodal perception, spectral feature learning and hybrid control switching to tackle the challenges of high-precision peg-in-hole assembly. By introducing SRM for robust visual representation and combining it with adaptive impedance control, the framework offers a distinctive solution that enhances sim-to-real transfer and generalization in complex assembly scenarios.