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Surveillance or support? policing harmful sexual behaviour among young people in schools
Get PDFThis paper examines the policing of harmful sexual behaviour (HSB) among young people in schools, drawing on qualitative research conducted with police and schools in southeast England. Utilising a Foucauldian surveillance perspective, we explore the challenges police experience in balancing punitive measures with relationship-building efforts. We highlight contradictions between policing objectives and strategies, with police engagement often emphasising surveillance, intelligence gathering and detection, including among officers endorsing relationship-based practice with young people. The overarching concern with behaviour management and discipline of young people in schools, combined with inadequate training and resourcing, perpetuates authoritarian policing practices, with implications for police–youth relations. We identify how tensions between deterrence and trust play out through a wider crisis of legitimacy regarding the capacity for legal frameworks and criminal justice to adequately capture and respond to HSB. We suggest these limitations undermine young people's rights, erode trust between young people and police, and ultimately compromise safety through hindering the effectiveness of HSB prevention and response efforts
What knowledge and skills do early career social workers need to practice effectively with children and families?
Get PDFThere is an extensive literature base about the knowledge and skills required by social workers to work effectively with children and their families. However, making sense of how best to translate this into direct practice can be challenging, particularly when newly qualified. The paper is based on a wider rapid evidence assessment (REA) of the post-qualifying knowledge and skills required by early career social workers (ECSWs) to practice effectively with children and their families. The REA involved searching for relevant English language literature (2012–2023) in ASSIA, Social Care Online, SocINDEX and PsycInfo and specialist journals. However, it was soon apparent that the literature was not segmented by career stage and wider searches relating to knowledge and skills needed to be undertaken. In total, 51 papers were included for review. Studies identified were largely qualitative, exploring the knowledge and skills required through observations of direct practice and self-report studies from the perspectives of social workers and children and families themselves. Where knowledge and skills were identified, relationship building and good authority skills were associated with some improved outcomes for family members. The review presents findings through practice-near descriptions of what works to build early career expertise with children and their families
The value of whole-face procedures for the construction and naming of identifiable likenesses for recall-based methods of facial-composite construction
Get PDFTraditional methods of facial-composite construction rely on an eyewitness recalling features of an offender's face. We assess the value of the addition of a trait–recall mnemonic to a cognitive-type interview, and perceptually stretching presented composites, to aid image recognition. Participant-constructors intentionally or incidentally encoded a target face, were interviewed about its facial features 3–4 h or 2 days later, made a series of trait attributions (or not) about the face and constructed a feature-based composite. Regardless of encoding manipulation, faces constructed after 3–4 h were twice as likely to be correctly named (cf. after 2 days) both when the trait–recall mnemonic was applied and composites were viewed stretched. Thus, the research indicates that benefit should be afforded when trait–recall mnemonics are employed for feature composites constructed on the same day as the crime and when composites are presented to potential recognisers with instruction to view the face as a perceptual stretch
Beyond movement alone:rethinking health implications of purposeful physical (in)activity, stillness, and rest
Get PDFPhysical inactivity has become associated with a range of poor health outcomes. Major policy documents have urged actions to decrease physical inactivity at a population level and health promotion campaigns have urged individuals to change their behaviour at a personal level. In this Viewpoint, we challenge the orthodox view, which frames physical activity as solely good and healthy, and inactivity as solely bad and unhealthy. Labelling inactivity as only unhealthy ignores underlying politics and contexts, neglecting the vital role that rest plays in sustaining both wellbeing and the capacity to care for others. To resist, we call for a holistic appreciation of stillness and rest as part of a Dynamic Continuum of Human Movement. A shift in thinking would consider the potential health benefits of stillness as being socially active, mentally active and restorative. We offer policy and practice implications, and encourage the public health community to incorporate an empathetic, inclusive and holistic appreciation of stillness into health promotion messaging
Defensive dentistry: perceptions and experiences among general dental practitioners in primary care
Get PDFIntroduction Although the term 'defensive dentistry' is documented, with a range of opinions around practising dentists, little is understood about the perception and the lived experiences of general dental practitioners (GDPs) about practising in a defensive way.Aim To explore how defensive dentistry is understood and experienced by GDPs working in primary care.Methodology An interpretive mixed-methods approach was adopted using an online questionnaire followed by semi-structured interviews. In total, 25 participants completed the questionnaire and six follow-up interviews were completed.Results Three main themes were identified: the sense of fear; practising the 'act of avoidance' with certain patients and/or procedures; and the overall impact on the profession.Conclusion GDPs unanimously identified the negative aspects of defensive practice as actions carried out for self-protection rather than the interests of patients. The reasoning for such practices included fear of litigation and/or investigation by the General Dental Council, heightening in recent years due to increased public awareness of litigation, and loss of public trust. Participants reported stress, low morale and deskilling as outcomes for practising defensively
Cellular signal detection by hydrogenated amorphous silicon photosensitive chip with electroexcitation
Get PDFBased on the photoconductive effect of photosensitive films, a designed light pattern was projected onto a hydrogenated amorphous silicon (a-Si:H) photosensitive chip to generate virtual light-induced electrodes for cellular electrical detection. To obtain high-quality cellular signals, this study aims to explore the effect of electrical excitation on a-Si:H photosensitive chip. Firstly, the electrochemical impedance spectroscopy (EIS) and volt-ampere characteristics of the a-Si:H photosensitive chip were characterized. EIS data were fitted to extract equivalent circuit models (ECMs) for both the chip and system. Then analogue experiments were performed to verify the ECMs, and the results were consistent with the circuit simulation. Finally, applied alternating current (AC) or direct current (DC) signals to the chip and recorded the electrical signals of the cultured cardiomyocytes on the a-Si:H photosensitive chip. The results demonstrated that applying a high-frequency small AC signal to the chip reduced the background noise of the system by approximately 85.1%, and applying a DC bias increased the amplitude of the detection signal by approximately 142.7%. Consequently, the detection performance of the a-Si:H photosensitive chip for weak bioelectrical signals was significantly enhanced, advancing its applicability in cellular electrophysiological studies
Representations of students on screen
Get PDFAny reader of Open Screens is likely to need little convincing of the importance screen cultures play in society. Rudine Sims Bishop (1990), when writing as an educator about the power of literature, emphasised how cultural representations function as windows into others’ lives and other worlds but also as mirrors – as a means of affirmation and identity formation, reflecting back experiences and offering a means to explore one’s own individual sense of belonging. Bishop’s (1990) work has informed many of the debates about representation explored in this issue. The term ‘students’ may conjure ideas and images informed by personal experience, but also by media representations of all kinds. When considering what these representations ‘do’, many questions arise: what can screen representations tell us about what it means to be a student? What experiences might students encounter and in what contexts? What does a student look like? What can representations of students tell us about a specific society and its central tenets and concerns? What students are represented, and who lacks representation? Many of the articles in this issue provide insight into several of these questions at once, helping to critically examine the construction of the student on screen. This introduction will provide a brief overview of some of the concepts and ideas about students on screen already in existence within academic publishing, before providing an overview of the Students on Screen project from which it stems. Finally, it will provide a summary of the issue to help you navigate it
Constructing the university student in British popular culture
No full textThe Students on Screen research project examines how screen representations of university students produce, frame, inform and contribute to complex understandings about what it might mean to be a university student. A limitation of the existing literature examining media representations of students is that much of this work is produced from an academic standpoint (including our own publications), rather than involving students in a co-produced analysis of their positioning as university students. With funding from Research England, three university students were appointed as research assistants to help examine three television dramas – Clique (BBC, 2017-2018), Normal People (BBC, 2020) and Big Boys (Channel 4, 2022-2025). Through this co-constructed analysis, we have sought to identify current concerns and anxieties about the lives and experiences of university students. In this paper, we explore themes relating to mental health, forms of exploitation, and transitions to adulthood. We also critically consider the positioning of the university itself, and to what extent higher education is currently framed as a ‘dark economy’ in British popular culture
Bayesian learning strategies for reducing uncertainty of decision-making in case of missing values
Get PDFBackground: Liquidity crises pose significant risks to financial stability, and missing data in predictive models increase the uncertainty in decision-making. This study aims to develop a robust Bayesian Model Averaging (BMA) framework using decision trees (DTs) to enhance liquidity crisis prediction under missing data conditions, offering reliable probabilistic estimates and insights into uncertainty. Methods: We propose a BMA framework over DTs, employing Reversible Jump Markov Chain Monte Carlo (RJ MCMC) sampling with a sweeping strategy to mitigate overfitting. Three preprocessing techniques for missing data were evaluated: Cont (treating variables as continuous with missing values labeled by a constant), ContCat (converting variables with missing values to categorical), and Ext (extending features with binary missing-value indicators). Results: The Ext method achieved 100% accuracy on a synthetic dataset and 92.2% on a real-world dataset of 20,000 companies (11% in crisis), outperforming baselines (AUC PRC 0.817 vs. 0.803, p < 0.05). The framework provided interpretable uncertainty estimates and identified key financial indicators driving crisis predictions. Conclusions: The BMA-DT framework with the Ext technique offers a scalable, interpretable solution for handling missing data, improving prediction accuracy and uncertainty estimation in liquidity crisis forecasting, with potential applications in finance, healthcare, and environmental modeling.</p
Advanced machine learning algorithms for blood pressure classification: early detection or prevention could save lives
Get PDFObjectives: The primary objective of the study is to classify the blood pressure (BP) levels using advanced machine learning (ML) techniques for predictive purposes. The study assesses the efficacy of the Naïve Bayes, AdaBoost, feedforward neural networks (FNNs), and long short-term memory (LSTM) algorithms over the conventional multinomial logistic model using standard performance evaluation metrics. Methods: The dataset comprised 15,000 entries obtained from the National Health Service, England, each containing eight variables. The variables include BP, age, weight, height, gender, smoking habit, alcohol consumption, and fitness level. The Naïve Bayes, AdaBoost, FNN, LSTM, and multinomial logistic models were employed in the study. Each model underwent training, testing, validation, and evaluation using suitable metrics such as accuracy, F1-Score, kappa statistics, sensitivity, specificity, and area under the curve score. Results: The FNN model gives the highest test accuracy of 89.47% and balanced performance, making it the most appropriate model for predicting BP levels. The LSTM model demonstrated strong proficiency in capturing temporal patterns. AdaBoost was highly effective for dealing with class imbalance, but Naïve Bayes was a dependable benchmark. The multinomial logistic model established a reliable and stable reference point. The results represented a notable improvement over previous research, which typically reported median accuracy rates in the 80–85% range. Conclusion: The study reveals that knowing an individual’s age, weight, height, gender, smoking habit, alcohol consumption, and fitness level is useful in predicting his/her BP level. Thus, the advanced ML algorithms demonstrate potential in accurately classifying BP levels and can aid in the prevention, detection, and management of hypertension
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