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VISION member awarded UKDS Impact Fellow to study violence and mental health impacts in older age

    Dr Anastasia Fadeeva

    VISION Research Fellow, Dr Anastasia Fadeeva, has been awarded a UK Data Service (UKDS) Fellowship.

    Anastasia’s interest and education in medicine increased her awareness of the impact of social determinants on people’s health. This led to an MSc in Public Health at London Metropolitan University followed by a PhD at Northumbria University and a career in health services and public health research.

    As a UKDS Fellow, Anastasia will look at the issues of violence in older age, the long-term impacts of violence on mental health, and the lack of reliable data.

    For more information about Anastasia and her work, see her blog on the UKDS website or email her at anastasia.fadeeva@citystgeorges.ac.uk.

    The UKDS is funded by the UKRI and houses the largest collection of economic, social and population data in the UK. Its Data Impact Fellowship scheme is for early career researchers in the academic or the voluntary, community, and social enterprise (VSCE) sector. The purpose of the programme is to support impact activities stemming from data-enhanced work. For further information on the UK Data Service please see: UK Data Service

    Photograph from Adobe Photo Stock subscription

    United to End Violence Against Women and Girls: An Online Animated Campaign  

      Violence against women and girls (VAWG) is a pressing issue in Iran, a Middle Eastern country marked by its patriarchal structure and systematic and pervasive gender discrimination. Educational programmes addressing this issue are scarce, and cultural barriers often hinder open discussion. The United to End Violence Against Women and Girls campaign aims to break this silence through a series of animated videos in Farsi and English and images designed to inform public discourse and to empower victims to seek support.

       The United to End Violence Against Women and Girls project was led by VISION researchers Ladan Hashemi and Sally McManus, in collaboration with colleagues from other UK universities including the University of Bristol, Goldsmiths University, Animation Research Centre at the University for the Creative Arts, and Leeds Beckett University. 

      They worked with an animation production team in Iran, a social media advisor, and two advisory groups. The advisory groups were Mehre Shams Afarid, an Iran-based non-governmental organisation (NGO), and IKWRO, a London-based charity providing services to women victims of violence from the Middle Eastern and North African (MENA) region—to incorporate culturally specific insights.

      Although the project initially focused on Iran, engaging with the UK-based NGO revealed an interest in extending its reach. As a result, English subtitles were added to make the animations accessible to a wider audience. This collaboration helped the content resonate with audiences both in Iran and within the global diaspora community, particularly those from the MENA region.

      The animations are grounded in evidence from a survey of 453 women in Iran, which explored the manifestation of various forms of VAWG in Iran and women’s perspectives on how to eliminate it. The survey was designed by Fatima Babakhani, CEO of Mehre Shams Afarid.

      Key findings from participants’ open-ended responses to the survey showed that, despite structural inequalities and deeply ingrained societal, cultural, and religious norms that perpetuate VAWG, change is possible through education and legal reforms.

      As one survey participant noted: “Unfortunately, many still don’t understand what violence truly is. Raising awareness is the solution.”

      The first four United to End Violence Against Women and Girls campaign animations focus on coercive control, economic abuse, technology-facilitated abuse, and active bystander interventions, with two more animations in development.

      With guidance from an Iranian social media advisor, a digital strategy was developed to maximise the campaign’s impact. Instagram was chosen as the primary distribution platform, as it is the most widely used social media platform in Iran, with over 47 million users. The animations are also shared on YouTube to further extend the campaign’s reach.

      Influencers and women’s rights activists with followings from thousands to millions were partnered with to amplify the campaign’s reach. The online campaign officially launched 25th November, on the International Day for the Elimination of Violence Against Women and Girls.

      By leveraging evidence-based content and strategic partnerships, we hope to spark meaningful conversations and drive change across Iran and the diaspora communities from the MENA region.

      Join us in raising awareness and advocating for change. Please follow and share the campaign links on your social media to help spread the message.

      Link to Instagram page

      Link to YouTube channel

      This project was funded by City St George’s, University of London Higher Education Impact Fund (HEIF) Knowledge Exchange and by the UKPRP VISION research consortium.

      For further information, please contact Ladan at ladan.hashemi@city.ac.uk

      Discovering the Potential of Large Language Models in Social Science Research: Takeaways from an Oxford Workshop

        By Dr Maddy Janickyj, Research Fellow in Natural Language Processing (NLP) for the Violence, Health, and Society (VISION) Consortium, University College London

        As a data-focused VISION researcher with a PhD specialising in Natural Language Processing (NLP; see our previous blog for more about this), I initially avoided ChatGPT and similar tools. ChatGPT, a type of Large Language Model (LLM) developed by OpenAI, offers capabilities like summarising information, translating text, and even coding.

        While ChatGPT is potentially the most well-known example of a LLM, similar models are integrated into many everyday tools. For instance, LLMs are the underlying technology in many customer service chatbots, virtual assistants like Alexa, and writing tools such as Grammarly. These LLMs are trained on large sets of data with the intention of getting them to understand (and in some cases generate) language. The models draw on this training to complete various tasks and are finetuned to work for specific domains. Their breadth of abilities and the many open-source models that have been developed make them the perfect methodological tool for researchers in both computer science and the social sciences. For clarity, an open-source LLM is one whose code and architecture are publicly available.

        To further understand how LLMs are being used by researchers and to consider how the tools would integrate with and support violence-related research, I – a mathematician turned computational social scientist – attended the Oxford LLMs workshop. The event, held at Oxford’s Nuffield College, aimed to bring early-career scholars up to speed with the technical foundations, real-world applications, and research potential of LLMs. Throughout the week, I met with PhD/Masters students and other Post-doctoral researchers interested in using LLMs to evaluate anything from economic, linguistic, and political issues, for example.

        Understanding LLMs: Lectures and Industry Insights

        The first few days provided foundational lectures and talks, showcasing the technical underpinnings and application of LLMs. One of the big draws was the calibre of speakers. We heard from industry experts working at well-known companies such as Meta, Ori , Qdrant, Wayfair, Intento, Arize AI, and Google.  

        We then started our deep dive into LLMs, including how they are trained and evaluated. We heard about the numerous ways you can fine-tune LLMs, a step which occurs after general pre-training and tailors a model to meet domain/task-specific needs. Fine-tuning methods such as Continued Pre-training, Supervised Fine-tuning, and Preference Tuning were highlighted. Each technique offers different ways of adapting LLMs to specialised domains without needing to re-train them from scratch, saving computational resources.

        We also covered common challenges associated with finetuning models. One of these is “catastrophic forgetting,” where a model’s performance declines in one area when it’s fine-tuned on another. For example, if a model is adjusted to improve name recognition, it may inadvertently lose accuracy in identifying locations. This side effect is something I encountered when finetuning other NLP models during my PhD and illustrates the balance required when refining LLMs.

        Applying LLMs: Collaborative Research Projects

        In the latter half of the week, workshop attendees collaborated on research projects, exploring LLM applications across social science realms. This was a hands-on opportunity to test LLM methodologies discussed earlier and apply them to real-world social science challenges.

        Leading up to the workshop we had the chance to review the proposed project briefs, gather literature showcasing how LLMs are used in our respective disciplines, and finally rank the four projects according to our own skillsets and research interests. One of the projects we decided to tackle as a group focused on developing an LLM purely for social science research. LLMs are considered to have some form of bias, for example against certain demographic groups, and with this ongoing project, we wanted to create a fair, unbiased, and open-source LLM suited to the social sciences.

        In another project, we examined gender bias in academia. For this, we used Google’s Gemini to classify the gender of authors in academic syllabi. By experimenting with prompts, we measured how well the LLM could assess gender trends in syllabus authorship. Using tools like Google Colab, we collaboratively coded and refined our approach, leveraging Gemini’s capabilities to highlight gender disparities effectively. In some cases, we found the model to correctly classify 100% of the authors’ genders. This project underscored both the potential and the limitations of LLMs in accurately capturing nuanced social issues.

        Appreciating the Potential: Be Cautious

        Overall, the Oxford workshop demonstrated how LLMs can be powerful tools in social science research including violence-related research such as what we do at VISION, provided they are tailored to specific domain needs and applied with caution. Hearing directly from researchers and industry professionals offered invaluable guidance on both leveraging and responsibly implementing LLMs. Its also important to consider the data you are utilizing and the outputs you are expecting. In my current area (which focuses on technology-facilitated abuse), an increasing number of researchers are using sensitive data and the outcomes of such research can impact the lives of real individuals. Thus, for anyone in the social sciences looking to integrate cutting-edge NLP methods, understanding the complexities behind these models and their applications is essential. I encourage readers to look at the work being done currently by the workshop participants, and to keep an eye out for later outputs of the workshop!

        For further information, please contact Maddy at m.janickyj@ucl.ac.uk

        Prior homelessness and associations with health and violent victimisation

          By Dr Natasha Chilman, Research Associate, UKRI Population Health Improvement (PHI-UK), Population Mental Health Consortium, Kings College London

          In the United Kingdom, we have the highest rate of homelessness compared to other high-income countries. For many people homelessness is a temporary, although often very impactful, experience in their lives. However, there is a paucity of research and data looking at people who are formerly homeless and living in private households (i.e., rented or owned accommodation).

          This blog describes a new study which fills this gap, conducted by Dr Natasha Chilman from King’s College London and colleagues, including Professor Sally McManus from VISION.

          The study authors analysed data from the Adult Psychiatric Morbidity Surveys, which is a nationally representative survey of adults living in private households in 2007 and 2014. Out of 13,859 people, 535 people reported previous experience of homelessness.

          Some of the key findings of the study were:

          • A staggering 40% of people who formerly experienced homelessness had experienced violence in their homes at some point in their lives, compared to 7% of people who had never been homeless.
          • A quarter (24%) of people who formerly experienced homelessness reported experience of sexual abuse, compared to less than 5% of people who had never experienced homelessness.
          • Almost half (45%) of the formerly homeless group were currently experiencing depression or anxiety, compared to just 15% of people who had never experienced homelessness. People who formerly experienced homelessness were also experiencing more severe symptoms of these common mental disorders.
          • There were strong associations between former homelessness and health conditions, across common mental disorders, physical health conditions, alcohol/substance dependence, and multimorbidities. These associations persisted even after adjusting for a range of potential confounders, including indicators of socio-economic position and smoking.
          • Adjusting for adverse experiences including violence and abuse attenuated associations between former homelessness and alcohol/substance dependence related health outcomes, but not mental/physical health.

          The findings from this study highlight the urgent need for long-term integrated healthcare support for people who are formerly homeless to continue after they have secured private housing. There were severe inequalities in experiences of violence and sexual abuse for people who have experienced homelessness, underscoring the importance of both violence and homelessness prevention, and of trauma-informed approaches to support.

          To read the article or download the paper free of charge:

          The public health significance of prior homelessness: findings on multimorbidity and mental health from a nationally representative survey | Epidemiology and Psychiatric Sciences | Cambridge Core

          To cite:

          Chilman N, Schofield P, McManus S, Ronaldson A, Stagg A, Das-Munshi J. The public health significance of prior homelessness: findings on multimorbidity and mental health from a nationally representative survey. Epidemiology and Psychiatric Sciences. 2024;33:e63. doi:10.1017/S2045796024000659

          Or for further information, please contact Natasha at natasha.chilman@kcl.ac.uk

          Empowering voices against ‘honour’-based abuse: A call to action

            There is an urgent need for specialist support for Middle Eastern, North African (MENA) and Afghan women and girls living in the UK, with many facing the particular risk of ‘honour’-based abuse (HBA).  On 18 October, the women’s rights organisation IKWRO held the impactful “Celebrating Courage: Empowering Voices Against Honour-Based Abuse” conference. It was hosted at City St George’s and proudly supported by the Violence and Society Centre (VASC) and VISION consortium.

            The powerful event featured art, presentations, panels, film, and spoken word performances that shed light on the often-overlooked experiences of ‘honour’-based abuse (HBA) and the urgent need for education and prevention.

            HBA disproportionately affects women and girls, it frequently goes unrecognised and is often conflated with domestic violence. Through this event, IKWRO, VASC and VISION aimed to illuminate the unique aspects of HBA and foster a deeper understanding of its implications.

            A diverse group of speakers, including survivors, advocates, lawyers, and researchers, shared their insights and experiences. This included powerful testimonies from victims/survivors, bravely recounting their experiences of HBA. Their voices resonated throughout the room, creating a sense of solidarity and support, while also providing a safe space for discussion.

            Over 100 people attended. A conference highlight was the strong presence of representatives from police and central government. Their attendance demonstrated a commitment to addressing HBA and offered them and other attendees a unique opportunity to engage directly with key figures at the forefront of the fight against HBA.

            One salient moment during the conference occurred when VISION researcher Dr Ladan Hashemi, during the discussion with the police officers regarding enhancements to law enforcement responses, emphasised that “violence constitutes violence, and abuse constitutes abuse, regardless of an individual’s identity or background. It is essential for law enforcement agencies to acknowledge their obligation to prevent and mitigate HBA in the United Kingdom”.

            The event also featured innovative approaches to communicating the issue of violence through art and spoken word. These forms of expression not only captivated the audience but also fostered a deeper understanding of the complexities surrounding HBA. By bringing art into a conference setting, IKWRO, VASC and VISION wanted to highlight the positive impact of creative communication, showcasing the experiences of survivors and the urgent need for change.

            In honour of the International Day of the Girl Child, this event was an important step toward raising awareness about HBA and the specific challenges faced by women and girls in the UK to police, government, specialist services, academic researchers and the general public.  A world free of abuse and violence requires the prevention of HBA, and for IKWRO that starts in their home base of London.

            Key to the event was the organisation and support of VISION’s Knowledge Exchange Manager, Kimberly Cullen, and Dr Hashemi. IKWRO, VISION, and VACS will continue to work collaboratively to elevate the voices of survivors and advocate for meaningful change in the fight against ‘honour’-based abuse.

            Photo caption: IKWRO and VISION. Photograph supplied by IKWRO.

            Green space may be important in the prevention of crimes

              The United Nations (UN) Sustainable Development Goals such as Goal 16, Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels, highlight the importance of using policy tools, for example urban planning, to prevent crimes. However, existing evidence of the association between green space and crime is mixed. Some studies indicate that the inconsistencies may be due to the variance in types of vegetation and the rates of crime reported across regions and countries.

              Therefore, UK Prevention Research Partnership funded consortia, GroundsWell and VISION, worked together to assess the conditional association between green space and crime. Groundswell researchers Dr Ruoyu Wang, Dr Claire L. Cleland, Dr Agustina Martire, Prof Dominic Bryan, and Prof Ruth F. Hunter collaborated with VISION researchers Dr Ruth Weir and Prof Sally McManus to consider the influence of vegetation type such as grassland and woodland, crime type such as violence and theft, and the rates of crime reported in Northern Ireland.

              They found that the association between green space and crime varies by vegetation type, crime type and rates of crime. The analyses showed that relatives were:

              • More grassland may be associated with lower crime rates, but only in areas with relatively low crime rates.
              • More woodland may also be associated with lower crime rates, but only for areas with relatively high crime rates.
              • The associations between green space and crime varied by type of crime.

              Check out their recent publication, Rethinking the association between green space and crime using spatial quantile regression modelling: Do vegetation type, crime type, and crime rates matter?, where they discuss their findings further as well as the implications for government approaches to consider green space as a potential crime reduction intervention. Policymakers and planners should consider green space as a potential crime reduction intervention, factoring in the heterogeneous effects of vegetation type, crime type and crime rate.

              To read the article or download free of charge:

              Rethinking the association between green space and crime using spatial quantile regression modelling: Do vegetation type, crime type, and crime rates matter? – ScienceDirect

              To cite:

              Wang, R., Cleland, C. L., Weir, R., McManus, S., Martire, A., Grekousis, G., Bryan, D., & Hunter, F. R. (2024). Rethinking the association between green space and crime using spatial quantile regression modelling: Do vegetation type, crime type, and crime rates matter?. Urban Forestry & Urban Greening.

              Illustration / photograph licensed under Adobe Photo Stock

              The story so far: Co-production in Lambeth

                By Elizabeth Cook, Senior Lecturer in Criminology & Sociology at City St George’s, University of London

                As the VISION consortium approaches the end of its third year, work continues on consolidating the learning from various large datasets in crime and justice, health, and specialist services.

                What we know is that these datasets are structured in different ways, collected by different agencies, and curated for quite different purposes. They represent particular ways of knowing about violence and abuse: they can help to identify patterns (e.g., what determines whether victim-survivors of sexual violence and abuse access support), prevalence (e.g., of workplace bullying and harassment), trends over time, and associations (e.g., between intimate partner violence, suicidality, and self-harm). However, we also know that large datasets struggle to capture the complex, and sometimes messy, realities of violence and abuse experienced by communities, especially those that are marginalised and minoritised.

                Peer action research in Lambeth

                In Lambeth, working in collaboration with peer researchers has made visible the evidence gaps that emerge at the intersection of multiple systems of inequality, including racism and misogyny.

                We are lucky to be partnered with Lambeth Peer Action Collective (LPAC), High Trees and Partisan as part of a peer action research project. The aim of the project is to explore the role that trusted adults and trusted spaces can play in protecting young people from exposure to violence. Currently, there are 11 peer researchers that work as part of the LPAC: a collective of young people and youth organisations campaigning for change in their community. They are supported by High Trees, a Community Development Trust in Tulse Hill, eight partner youth organisations, and Partisan, a Black-led Community Interest Company providing culturally sensitive mental health support.

                What has been achieved so far?

                The project builds upon research conducted by the previous cohort of LPAC researchers conducted between December 2021 and August 2022. This project identified the impacts of violence on young people in Lambeth and the structural conditions of poverty, housing, education, urban regeneration, and public safety that were experienced unequally across the community.

                Developing these findings further, the second cohort of peer researchers have been participating in weekly research training sessions led by High Trees and supported by VISION. The group has been learning everything they need for the next stage: from safeguarding and finances, to developing research questions, critical thinking skills, and how to evaluate research methods. This month, the LPAC researchers are getting ready to put into practice the interview skills that they have been learning each week in preparation for the next stage of the project – recruitment.

                There has been amazing progress so far – not only in forming a research question and defining key concepts, but in developing a shared space for researchers to feel like change is possible and to collaborate with others who want the same.

                What have we learned?

                There are ongoing conversations about how peer action research can work to redress the imbalance between ‘researcher’ and ‘researched.’ These conversations seem even more relevant to research on violence and abuse, where the issue of power is central to both.

                So far, the weekly sessions with peer researchers as well as our meetings with High Trees have taught us a lot about how power operates within institutions and the ways that it can be shared if there is a will to share it. This can be reflected in adequate resourcing, decision-making, access, and sharing skills and knowledge. The project has underlined the importance of respect in research: for different forms of expertise, within spaces, and within research relationships. The project has also challenged adult-centric assumptions about what we suppose that young people need to live better lives.

                As mentioned previously, this project highlights the evidence gaps that occur at the intersection of multiple inequalities. In doing so, peer action research can also shape how we utilise large datasets, recognising how different social realities are reflected within existing data (or not).

                In this sense, this collaboration has also made hyper-visible the question of: what and who is research for? As others have suggested, action research is not so much a methodology, but a way of thinking about research: it is a way of approaching a specific problem through community, participation, and curiosity. It is not necessarily driven by knowing more about something, but by wanting to change something with what you know.

                We hope that this research continues in that spirit!

                Further information

                Do check out the LPAC’s manifesto for change and their previous report!

                 Photograph is copyrighted to Lambeth Peer Action Collective and not for use.

                Natural Language Processing: Improving Data Integrity of Police Recorded Crime

                  By Darren Cook, Research Fellow in Natural Language Processing at City, University of London

                  Did you know that police recorded crime data for England and Wales are not accredited by the UK’s Office for Statistics Regulation (OSR)? This decision, made by the OSR after an audit in 2014, was due to concerns about the reliability of the underlying data.

                  Various factors affect the quality of police-recorded data. Differences in IT systems, personnel decision-making, and a lack of knowledge-sharing all contribute to reduced quality and consistency. Poor data integrity leads to a lack of standardisation across police forces and an increase in inaccurate or missing entries. I recently spoke about this issue at the Behavioural and Social Sciences in Security (BASS) conference at the University of St. Andrews, Scotland.

                  Correcting missing values is no small feat. In a dataset of 18,000 police recorded domestic violence incidents, we found over 4,500 (25%) missing entries for a single variable. Let’s assume it takes 30 seconds to find the correct value for this variable – that’s 38 hours of effort – almost a full working week. Given that there could be as many as twenty additional variables, it would take over four months to populate all the missing values in our dataset. Expanding such effort across multiple police forces and for multiple types of crime highlights the inefficiency of human-effort in this endeavour.

                  In my talk, I outlined an automated solution to this problem using Natural Language Processing (NLP) and supervised machine learning (ML). NLP describes the processes and techniques used by machines to understand human language, and supervised ML describes how machines learn to predict an outcome based on previously seen examples. In this case, we sought to predict the relationship between the victim and offender – an important piece of demographic information vital to ensuring victim safety.

                  The proposed system would use a text-based crime ‘note’ completed by a police officer to classify the victim offender relationship as either ‘Ex-Partner”, “Partner”, or “Family” – in keeping with the distinction made by Women’s Aid. Crime notes are an often-overlooked source of information in police data, yet we found they consistently referenced the victim-offender relationship. The goal of our system, therefore, was to extract the salient information from the free-form crime notes and populate the corresponding missing value in our structured data fields.

                  Existing solutions based on keywords and syntax parsing are used by multiple UK police forces. While effective, they require manual effort to create, update, and maintain the dictionaries, and they don’t generalise well. Our supervised ML system, however, can be automatically updated and monitored to maintain accuracy.

                  When tested, our system achieved 80% accuracy, correctly labelling the relationship type in four out of five cases. In comparison, humans performed this task with approximately 82% accuracy – an arguably negligible difference. Moreover, once trained, our system could classify the entire test set (over 1,000 crime notes) in just sixteen seconds.

                  However, we noted some limitations, the biggest of which was a high linguistic overlap in crime notes between ‘Ex-Partner’ and ‘Partner’ that caused several misclassifications. We believe more advanced language models (i.e., word embeddings) will improve discrimination between these relationships.

                  We also discovered a potential prediction bias against minorities. Although victim ethnicity wasn’t included in our training setup, we observed reduced accuracy for Black or Asian victims. The source and extent of this bias are subjects of ongoing research.

                  Our findings highlight the promise of automated solutions but serve as a cautionary tale against assuming these systems can be applied carte blanche without careful consideration of their limitations. Several outstanding questions remain. Is a system with 80% accuracy good enough? Is it better to leave missing values rather than predict incorrect ones? Incorrectly identifying a perpetrator as a current partner rather than an ex-partner could significantly impact the victim’s safety. Additionally, a model biased against certain ethnicities risks overlooking the specific needs of minority groups.

                  The conference sparked lively and engaging conversation about many of these issues, as well as the role that automation can be play within the social sciences more broadly. A research article describing these results in full is the focus of ongoing work, and the presentation slides are available below as a download.

                  For further information please contact Darren at darren.cook@city.ac.uk or via LinkedIn @darrencook1986

                  Dr Darren Cook, An application of Natural Language Processing (NLP) to free-form Police crime notes – 1 download

                  D Cook_NLP and PoliceDownload

                  Photo by Markus Spiske on Unsplash

                  Uncovering ‘hidden’ violence against older people

                    By Dr Anastasia Fadeeva, VISION Research Fellow

                    Violence against older people is often overlooked. As a society, we often associate violence with young people, gangs, unsafe streets, and ‘knife crime’. However, violence also takes place behind front doors, perpetuated by families and partners, and victims include older people. 

                    Some older people may be particularly vulnerable due to poorer physical health, disability, dependence on others, and financial challenges after retirement. Policy rarely addresses the safety of this population, with even health and social care professionals sometimes assuming that violence does not affect older people. For example, doctors may dismiss injuries or depression as inevitable problems related to old age and miss opportunities to identify victims (1). In addition, older people may be less likely to report violence and abuse because they themselves may not recognise it, do not want to accuse family members, or out of fear (2). 

                    Given victims of violence often remain invisible to health and social services, police, or charities, the most reliable statistics on violence often come from national surveys such as the Crime Survey for England and Wales (CSEW) conducted by the Office for National Statistics. However, for a long time the CSEW self-completion – the part of the interview with the most detail on violence and abuse – excluded those aged 60 or more, and only recently extended to include those over 74. Some national surveys specifically focus on older people, but these ask very little about violence and abuse. Additionally, despite people in care homes or other institutional settings experiencing a higher risk of violence, it can be challenging to collect information from them. Therefore, many surveys only interview people in private households, which excludes many higher-risk groups.

                    We need a better grasp of the extent and nature of violence and abuse in older populations. First, reliable figures can improve the allocation of resources and services targeted at the protection of older people. Second, better statistics can identify the risk factors for experiencing violence in later life and the most vulnerable groups.

                    In the VISION consortium, we used the Adult Psychiatric Morbidity Survey (APMS 2014) to examine violence in people aged 60 and over in England (3). While we found that older people of minoritised ethnic backgrounds are at higher risk of violence (prevalence of 6.0% versus 1.7% in white people in 12 months prior to the survey), more research needs to be done to distinguish the experiences of different ethnic groups. Our research also showed that loneliness and social isolation were strongly related to violence in later life. Older people may experience social isolation due to limiting health issues or economic situations, and perpetrators can exploit this (4). Moreover, isolation of victims is a tool commonly used by perpetrators, especially in cases of domestic abuse (5).  Knowing about these and other risk factors can help us better spot and protect potential victims.

                    Additionally, more needs to be learnt about the consequences of life course exposure to violence for health and well-being in later life. This is still a relatively unexplored area due to limited data and a lack of reporting from older victims and survivors. It is sometimes more difficult to establish the link between violence and health problems because the health impacts are not always immediate but can accumulate or emerge in later life (6). Also, as people develop more illnesses as they age, it is more challenging to distinguish health issues attributable to violence. Therefore we are also using the English Longitudinal Study of Ageing (ELSA) to examine temporal relationships between lifetime violence exposure and health in older age.

                    Dr Sophie Carlisle, Evaluation Researcher at Health Innovation East Midlands, and former VISION researcher, also reflects on violence against older people and includes an analysis of our study’s strengths and weaknesses in her 10 December 2024 blog on the Mental Elf website, Violence against older people – linked to poor mental health #16DaysOfActivism2024. Sophie highlighted how the study reported that violence against older people is often perpetrated by an intimate partner and is strongly associated with poor mental health.

                    In an inclusive society, every member should be able to lead a life where they feel safe and respected. We are delighted that the CSEW has removed the upper age limit to data collection on domestic abuse, which is one step towards making older victims and survivors heard. Continuous work on uncovering the ‘hidden’ statistics and examining the effects of intersectional characteristics on violence is crucial in making our society more inclusive, equal, and safe for everyone. For example, one VISION study (7) has demonstrated that the risks of repeated victimisation in domestic relationships had opposite trends for men and women as they aged. We are committed to support the Hourglass Manifesto to end the abuse of older people (8), and are willing to provide decision makers with evidence to enable a safer ageing society.

                    For further information, please see: Violence against older people and associations with mental health: A national probability sample survey of the general population in England – ScienceDirect

                    Or please contact Anastasia at anastasia.fadeeva@city.ac.uk

                    Footnotes

                    • 1.  SafeLives U. Safe later lives: Older people and domestic abuse, spotlights report. 2016.
                    • 2.  Age UK. No Age Limit: the blind spot of older victims and survivors in the Domestic Abuse Bill. 2020.
                    • 3.  Fadeeva A, Hashemi L, Cooper C, Stewart R, McManus S. Violence against older people and mental health: a probability sample survey of the general population. forthcoming.
                    • 4.  Tung EL, Hawkley LC, Cagney KA, Peek ME. Social isolation, loneliness, and violence exposure in urban adults. Health Affairs. 2019;38(10):1670-8.
                    • 5.  Stark E. Coercive control. Violence against women: Current theory and practice in domestic abuse, sexual violence and exploitation. 2013:17-33.
                    • 6.  Knight L, Hester M. Domestic violence and mental health in older adults. International review of psychiatry. 2016;28(5):464-74.
                    • 7.  Weir R. Differentiating risk: The association between relationship type and risk of repeat victimization of domestic abuse. Policing: A Journal of Policy and Practice. 2024;18:paae024.
                    • 8.  Hourglass. Manifesto A Safer Ageing Society by 2050. 2024.

                    Photo from licensed Adobe Stock library

                    Un-Siloing Securitization: An intersectional intervention

                      By Dr Alexandria (Andri) Innes, VISION researcher and Senior Lecturer in International Politics at City, UoL

                      This research makes a case for shifting how we use and think about securitization theory. Securitization theory conventionally offers some insight into how certain issues are brought under the umbrella of security – normally state security – rather than sitting in normal political debate. When something is securitized more extreme or authoritarian policies that would normally be controversial in liberal democracies can be used. This might include things like removing civil liberties such as freedom of speech or freedom of assembly, or indefinite detention, or even policies that we’re all familiar with from 2020 and 2021, prohibiting freedom of association and freedom of movement in public space.

                      Securitization theory has focused on process (how something becomes securitized), object (what is securitized), and subject (who is being protected). The latter is generally the state and/or society. The process works through a meaningful speech act suggesting something is a security issue or framing it in security language (think about the war on drugs or the war on terror). The speech act then has to be accepted by an audience, who might be society at large, or the public, but also might be specialist practitioners, policy makers, think tanks, civil society, educators and so on. And the object of securitization is anything where this type of totalising discourse is evident. Examples include health, transnational crime, climate change, religion, humanitarianism, terrorism, particular ethnic identities, and immigration along with plenty of other things.

                      In this article, I argue that we should consider inequality when deconstructing and attempting to understand the process and practice of securitization. I suggest that racialization, ethnicization, and gendering create structural inequality in the ordering of what we think of as international – a world composed of equal state units. The nation state relies on these processes to function as an identity unit in the way that it does (with passport carrying, rights-bearing citizens and the right to deny rights to people who are not in the correct in-group). I propose that securitization theory might do better at dealing with inequality of we focus on the experience of being securitized, more so than the speech acts that make that securitization happen.

                      The article functioned more as a review of this sub-paradigm, and turns attention to the way the ‘object’ part tends to be siloed into the relevant thematic areas. So we look to just one securitized object at a time. Here, the article looks instead at three processes of securitization, to show that the siloing means the forms of inequality inherent in the nation state and national security are reproduced rather than reckoned with.

                      I look at the securitization of health, the securitization of immigration, and the securitization of gender-based violence. I suggest by mapping these objects of securitization together, we can better see the intersectional violence of inequality played out, and make visible the vulnerability, inequality and violence that pre-exists securitization, but is also enhanced, aggravated and at times hidden by it.

                      For further information please see: Un-siloing securitization: an intersectional intervention | International Politics (springer.com)

                      Or contact Andri at alexandria.innes@city.ac.uk

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