bluuebunny/crossref_metadata_2025 huggingface dataset:
Indian School Student Scientific Approach on Influenza Fever
Authors: Gayathri Oviyaa Murugan
Published: September 2023

First report in the world about Influenza fever from a school student with her own experience of Influenza fever and symptoms
Measuring Vulnerability and Resilience in the Time of Change Using Small Area Estimation
Authors: DeSalvo Bethany
Published: September 2023

The United States Census Bureau launched a new tool for national agencies and local communities, the Community Resilience Estimates (CRE). The CRE tracks social and economic vulnerability by measuring the capacity of individuals and households to cope with the external stresses of the impacts of a disaster.

From the beginning of the pandemic, the negative effects of COVID-19 have been strongly related to certain individual and household characteristics. With access to granular microdata from the Census Bureau, the CRE maps the risk assessment of local populations down to the neighborhood level and allows national and community leaders to more efficiently respond to emergencies.

Federal statistical agencies are uniquely positioned to provide the most accurate and timely measures for an individually focused community resilience indicator. We use detailed demographic and economic data about individuals to build these estimates. Having the richest data sources, the federal government can produce estimates with the most granularity, highest statistical quality, and broadest coverage, while still protecting privacy. We do this through modeling multiple sources of data using small area estimation techniques.

This work will soon be built out to include exposure data (wind, flood, heat, fire, etc) so that federal agencies and researchers can perform experimental studies for better evidenced based policy making and evaluation.
The time is now: Preventing unnecessary criminalisation of children in care
Authors: Hunter Katie
Published: September 2023

ObjectivesNewly linked administrative datasets allow us to explore the relationship between ethnicity, care experience and youth justice involvement. This data reveals huge disparities in the youth justice system, highlighting the need for urgent policy reform to prevent unnecessary criminalisation of children in care and care leavers.
MethodsUsing linked administrative datasets from the Ministry of Justice and Department for Education, this paper takes a birth cohort approach to understanding youth justice involvement. It draws on National Pupil Database, Children Looked After and Police National Computer records from 2006 to 2017. Firstly, this paper will use descriptive statistics to highlight disparities in the extent of youth justice involvement among care-experienced individuals compared to non-care-experienced individuals and how this varies by ethnic group. Focusing on three offence types, the paper will then draw on mixed effects models to assess the likelihood of receiving a harsh sentence.
ResultsThe paper will show that the extent of youth justice involvement among individuals who have been in care (i.e. foster care, children’s homes and kinship care) is much greater than previously thought. It will demonstrate that youth justice involvement is especially common among ethnic minority care-experienced individuals, particularly those who identify as Black or mixed ethnicity. The paper will highlight how care experience increases a person's risk of receiving a custodial sentence and may also affect their risk of getting a longer custodial sentence. Findings will be situated within the broader literature on in-care criminalisation (e.g. Walklate & Fitz-gibbon, 2021), calling attention to institutional and structural influences on justice systems involvement.
ConclusionThe paper will show how linked administrative data can generate new insights which can inform policy and practice across both systems of care and justice. In doing so, it will make the case for a statutory duty of preventing unnecessary criminalisation of children in care and care leavers."
Is obesity more likely among children sharing a household with an older child with obesity?
Authors: Firman Nicola , Wilk Marta , Marszalek Milena , Griffiths Lucy , Harper Gill , Dezateux Carol
Published: September 2023

ObjectivesWe used a dynamic method of identifying household members from Electronic Health Records (EHRs) linked to National Child Measurement Programme (NCMP) data to estimate the likelihood of children with obesity sharing a household with an older child with obesity, accounting for individual and household characteristics.

MethodsWe included 126,829 NCMP participants in four London boroughs and assigned households from encrypted Unique Property reference Numbers (UPRNs) at NCMP date for 115,466 (91%). We categorised the ethnic-adjusted body mass index of the youngest and oldest household child (underweight/healthy weight<91st, ≥91st to <98th overweight, obesity≥98th centile) and explored associations of the youngest child’s weight status with: oldest child’s weight status, number of household children (two, three or ≥4), youngest child’s sex, ethnicity and school year of NCMP participation (reception or year 6). We estimated adjusted odds ratios (aOR) and 95% confidence intervals (CI) of obesity in the youngest child.

Results19,702 UPRNs were shared by two or more NCMP participants (youngest children: 51.2% male, 69.5% reception). 10.4% of youngest (95% CI: 10.0,10.9) and 13.0% of oldest (12.5,14.3) children were living with obesity. One third of youngest children with obesity shared a household with another child with obesity (33.2%; 31.2,35.2), compared with 9.2% (8.8,9.7) of those with a healthy weight. Youngest children living with an older child with overweight (aOR: 2.33; 95% CI: 2.06,2.64) or obesity (4.59, 4.10,5.14), those from South Asian ethnic backgrounds (1.89; 1.64,2.19) or taking part in NCMP in year 6 (2.21; 2.00,2.43) were more likely, and girls (0.73; 0.67,0.81), children living with just one other child (0.87; 0.77,0.98) and from Black ethnic backgrounds (0.78; 0.66,0.93) less likely, to be living with obesity.

ConclusionLinked EHRs can provide novel insights into the shared weight status of children sharing the same household. Further qualitative research is needed to understand how household food practices may vary by other household characteristics to improve our understanding of how the home environment influences childhood obesity.
Multi-systemic risk and protective factors for contact with criminal justice services for vulnerable children
Authors: Leyland Anna
Published: September 2023

Children who experience adversity in childhood are more likely than their peers to have contact with criminal justice services. There are many risk and protective factors that can influence outcomes for vulnerable children, including factors within the child, family, school, community and the wider socio-political context. Little is currently understood about how these multi-systemic factors impact on individual outcomes, and how the formal response from child protection services may improve outcomes for vulnerable children. Through the use of a whole population administrative dataset and multi-level models the present findings explore the role of multi-systemic factors on later involvement with criminal justice services in England. In particular, the complex interactions between childhood adversity, ethnicity, poverty, additional educational needs and disabilities, engagement in education, and the level of school based and community child protection interventions. The research also reports on how individual outcomes are influenced by community level inequalities and public sector service resources, that differ between regions of England. The findings uphold the importance of recognition of vulnerable children as a heterogenous group whose outcomes are strongly influenced by multi-systemic factors, which could be influenced directly by policy and practice changes. The presentation also highlights the potential contribution and limits of administrative data to the field.
The employment, retention and exit of publicly employed nurses in New Brunswick, Canada: An analysis using linked administrative data
Authors: McDonald Ted
Published: September 2023

ObjectivesAs in many jurisdictions, New Brunswick (Canada) is facing an acute and continuing shortage of registered nurses as recruitment fails to keep pace with retirements and resignations. The purpose of this retrospective study is to analyze the recruitment, retention and exit decisions of nurses in the NB public health system.

MethodsThe analysis will use a unique linked administrative data set that combines individual-level nurse employment data, immigration landing records, university graduation data and Medicare health insurance registry data on all publicly employed registered nurses in NB as well as individuals who graduated from a University nursing program in NB and immigrants to NB who previously trained as nurses in their home country. Data are provided by multiple government departments and are accessed through the NB Institute for Research, Data and Training. The analysis will include both descriptive statistics and econometric methods appropriate to the particular outcome of interest.

ResultsThe analysis will present results on four dimensions of nursing employment. The first is transitions from nursing programs in NB universities into employment in the NB public health system. The second is transitions of internationally educated nurses into employment as nurses in the NB public system and the timing of those transitions, which will reflect the process of credential evaluation, training and licensure. The third is exits from employment in the public health system, with consideration of both retirement and pre-retirement departures. The fourth is mobility decisions of those nurses exiting employment and whether they remain in the province after leaving employment. The potential effects of a range of demographic, geographic and health system level factors on these outcomes will be considered.

ConclusionAnalysis of entry to and exit from nursing employment in NB and factors associated with those dynamics will be vital for health resource planning for a province dealing with growing labour shortages. The unique nature of the linked data will also generate important insights for other jurisdictions facing similar challenges.
A synthetic data strategy for Scotland: Using synthetic data to improve access to public sector data for research
Authors: Adair Lynne
Published: September 2023

ObjectivesResearch Data Scotland (RDS) is working to improve the economic, social and environmental wellbeing in Scotland by enabling access to, and linkage of, public-sector data for research in the public good. Our objective was to explore how synthetic data can be used to support this aim.

MethodsWe investigated what other, similar, data organisations were doing around synthetic data, both in Scotland and beyond, and the issues to consider. We discussed use cases, tools, level of fidelity of synthesis, disclosure risk, information governance (IG) requirements, where the synthetic data might sit, who could access it, training and accreditation requirements, and barriers to synthesis. The findings were used to draft a synthetic data strategy, create a working group and plan future work. We also held a user workshop to discuss researcher requirements and asked our public panel about their understanding and concerns around synthetic data.

ResultsThree workstreams have been set up around disclosure risk & IG, synthesis, and access, promotion & engagement. A test synthesis of education data is planned. From our user workshop one of the main themes that emerged was the usefulness of low fidelity, unlinked datasets for data discovery purposes at the early stages of a project. Other areas where synthetic data can improve data access are for training, where synthetic data can be used to upskill users in the use of administrative data, and in code development, to allow earlier access to data for code generation and to reduce the time needed in secure settings and with access to real data. Future work will bring together data controllers to discuss disclosure risk.

ConclusionSynthetic data can potentially be used to improve and speed up data access. RDS will fund and support synthetic data projects to demonstrate value, and work with data controllers to produce synthetic datasets on an ongoing basis, for public sector research datasets in Scotland.
Incidence and age at diagnosis of 31 long-term conditions and multimorbidity by sex and social class in a Scottish population
Authors: Fagbamigbe Adeniyi , Agrawal Utkarsh , McCowan Colin
Published: September 2023

A good understanding of age, when individuals develop individual conditions and subsequently become multimorbid, would provide evidence to support better prevention, detection, and management of multiple long-term conditions. The study aimed to assess the age at onset, incidence, and prevalence of 31 long-term conditions and multimorbidity in Fife and Tayside, Scotland by sex and socioeconomic status.

We linked routinely collected health records of 431,772 patients aged 25 years or higher on January 1st, 2000 till December 31st, 2018 from several database. Age of first report of the 31 Elixhauser long-term conditions was assessed.

Median age at the onset of conditions within the population ranged from 39 years for AIDS/HIV to 73 years for fluid and electrolyte disorders and 75 years for renal failure. This was mostly delayed among the least deprived than most deprived people, up to 14 years for psychoses and AIDS/HIV. People with the highest deprivation in both sexes had earlier onset of all conditions except for blood loss anaemia where most deprived males had three years delay. Median age at the onset of multimorbidity and complex multimorbidity was 67 years (68 for females vs 66 for males; 68 for most deprived vs 73 for least deprived) and 71 years (73 for females vs 70 for males; 73 for most deprived vs 78 for least deprived) respectively. The yearly incidence of both multimorbidity and complex multimorbidity rose steadily from 1.6% and 0.2% in 2000, peaked in 2015 (3.2% vs 1.4%), declined to 2.9% and 1.3% respectively in 2018 while the prevalence grew from 1.6% and 0.2% to 27.2% and 10.1% over the same period.

A number of conditions had earlier onset while others were delayed. The onset of conditions is generally about five years earlier among most deprived people than the least deprived just as multimorbidity and complex multimorbidity is higher and earlier among the most deprived. This information could help drive health system policy, management, intervention, and allocation of resources.
Emergency Department presentations with suicidal ideation: A missed opportunity for intervention?
Authors: Ross Emma , Maguire Aideen , O'Hagan Denise , O'Reilly Dermot
Published: September 2023

ObjectivesSuicidal ideation constitutes a central element of most theories of suicide. Despite its prevalence, most research has focused on other suicidal behaviours such as self-harm. This study examines the characteristics of those presenting to EDs with suicidal ideation and quantifies the associated risk of suicide and other causes of death.

MethodsThis retrospective cohort study used population-wide health administration data linked to data from the Northern Ireland Registry of Self-Harm and centrally held mortality records from April 2012 to December 2019. Mortality data, coded as suicide, all-external causes and all-cause mortality was analysed using Cox proportional hazards. Additional cause-specific analyses included accidental deaths, deaths from natural causes, and drug and alcohol-related causes.

ResultsThe final cohort comprised 1,662,118 individuals aged over 10 years, of whom 15,267 presented to the ED with ideation during the study period. Individuals with ideation had a ten-fold increased risk of death from suicide (HRadj=10.84, 95% CI 9.18, 12.80) and from all external causes (HRadj=10.65, 95% CI 9.66, 11.74) and a three-fold risk of death from all causes (HRadj= 3.01, 95% CI 2.84, 3.20). Further cause-specific analyses indicated that risk of accidental death (HRadj=8.24, 95% CI 6.29, 10.81), alcohol-related death (HRadj=10.57, 95% CI 9.07, 12.31), and drug-related death (HRadj=15.17, 95% CI 11.36, 20.26) were also significantly raised. There were few socio-demographic and economic characteristics that would identify which of these patients are most at risk of suicide or other causes of death.

ConclusionsThe identification of those experiencing ideation is recognised to be important yet difficult in practice. The ED represents an important potential intervention point for this hard-to-reach population. However, and unlike those presenting with self-harm, clinical guidelines for the management and recommended best practice and care of these individuals are lacking.
Exploring public perceptions of the public good use of data for research and statistics
Authors: Kashef Shayda , Nickson Sofi , Cowan Mary
Published: September 2023

ObjectivesSharing and use of data for research and statistics underpins creation of better evidence for decision-making. For public data to be shared among organisations, the research or statistics created with the data must serve the public good. We argue decisions about what public good means should involve the public.

MethodsWe carried out a public dialogue to better understand public perceptions of the ‘public good’ use of data for research and statistics. This included deliberative in-person and online workshops with 68 people in Belfast, Cardiff, Glasgow, and London. The participant demographics were purposefully diverse with little to no formal knowledge of data or statistics. Through discussions in small groups, and knowledge exchange with data and statistics professionals, participants had the opportunity to hear more about statistics and data sharing and linkage, and could share their insights into this work from a personal perspective.

Results24 hours of deliberative discussions were analysed and distilled into five main findings, which demonstrate that participants want the public to have a say in how the public good is defined in the context of decision-making on data sharing and use. The participants also strongly expressed that data for research and statistics should address inequalities in society and minimise harms; both of which could be partly achieved through meaningful public engagement. Participants also suggested that they would like to see more communication around the benefits of the use of data for research and statistics. Finally, they were broadly supportive of more data sharing under best practice safeguards, over and above existing legal frameworks.

ConclusionInsights from deliberative discussions with members of the public on their perceptions of the public good use and sharing of data is reciprocally beneficial. It demonstrates trustworthiness to the public by meaningfully involving them in decisions about their data and ensures organisations can receive feedback on how to maximise the public good of their work.
Comparing the 2021 Census to administrative data to better understand the population estimation challenge
Authors: Pereira Elizabeth , Robertson Zak , Barnes Ayesha , Jones Ffion , Mylles Sally , Pearce Chloe
Published: September 2023

ObjectivesTo analyse the linked dataset between England and Wales 2021 Census/Census Coverage Survey and the Demographic Index (DI). This is a rare and unique opportunity to make direct comparisons between Census and administrative data to provide evidence on improvements in the use of administrative data in population and migration statistics.

MethodsThe DI is a composite dataset made up of several linked administrative sources. The linked output is complex, made up of individual sources grouped into clusters. These were linked to the Census, using both automatic and clerical matching to obtain the highest matching quality possible. To carry out the analysis, a flagging strategy has been designed to enable analysts to form cuts of the linked dataset that are specific to their research needs. High level research questions were designed to compare the coverage and capture across the Census and administrative data.

ResultsInitial analysis has been completed and reviewed, including: comparing the coverage of the DI and Statistical Population Datasets (which use inclusion rules to approximate the usual resident population) to Census, comparing how Communal establishments are captured in the DI and Census and comparing the geography of those found in both the DI and the Census.

The results will inform the National Statistician’s 2023 Recommendation, but also the future delivery of transformed population and migration statistics. This includes:

Providing insights to improve the DI’s linkage methodology

The SPD’s construct

Identifying the extent of over and under coverage in the SPD, allowing development of an estimation strategy to estimate the population using administrative data more accurately

ConclusionThis opportunity to compare linked administrative and Census data enables analysis to provide unique insights on quality and coverage, to improve the use of administrative data to inform population estimates, provide evidence on the use of additional administrative sources and improve future linkages.
Hidden in plain sight: Using administrative data to conduct a longitudinal cohort study of children exposed to opioids in pregnancy
Authors: Marryat Louise , Rauchhaus Petra , Boardman James P , McFadden Alison , Whittaker Anne , Frank John
Published: September 2023

ObjectivesChildren of women who use substances are difficult to research at a population-level using traditional research methods due to the complexity of their lives. Resultingly, we have little robust evidence on their outcomes. This study developed an administrative data cohort of children exposed to opioids and explored health outcomes.

MethodsUsing data from birth records, antenatal records, prescription data, hospital/psychiatric hospital admissions, and drug and alcohol service data, we identified 6,408 children (born 2009-2019) in Scotland who were exposed to opioids through illicit use and/or medication assisted treatment (i.e. methadone/buprenorphine). A control group (n. 19,089) of children not exposed to opioids were matched on age of mother and Scottish Index of Multiple Deprivation. Data were described and linear and logistic regression models were used to examine the relationship between risk factors (such as drug and alcohol use in pregnancy, gestation at booking and at birth), and key early outcomes.

ResultsAlthough the majority of women had their substance use recorded in antenatal records, 28.9\% did not, demonstrating the importance of using multiple administrative datasets to form the cohort. Children in the cohort were more likely to experience a range of adverse outcomes including being born early (17\% born prematurely, compared with 6.5\% in control group), having a below normal Apgar score (the scoring system used to assess newborns shortly after birth) (2.9\% in cohort vs. 1.5\% in controls), having significantly lower birthweight, length and head circumference, and more likely to be removed from their mother prior hospital discharge. Differences between the cohorts remained after controlling for other risk factors including alcohol use, and gestation.

ConclusionThis feasibility study brought together a cohort of children usually excluded from traditional forms of research. The research demonstrated early differences in outcomes between exposed children and others from similar socio-economic groups. The next stage of this research is exploring health and development outcomes in the preschool period.
Ethnicity, gender and community sentences
Authors: Sorsby Angela
Published: September 2023

ObjectivesThis paper presents findings from administrative data analysis examining differences between ethnic groups and men and women in the number and type of requirements that make up community sentences as well as the effectiveness of different requirements in terms of successful completion of the sentence.

MethodsThe paper presents findings from analysis of the Data First probation and criminal justice linked datasets. The analysis will focus on whether:

there are differences between ethnic groups and men and women in the number and type of requirements that make up community-based orders (rehabilitation, unpaid work, curfew and accredited programmes)

some requirements are more effective in terms of successful completion of the order.

The paper presents findings from regression analysis used to examine the above relationships while controlling for other relevant variables such as age, number of previous convictions and offence type.

FindingsGraphs will be used to set out differences between broad ethnic groups and men and women in the total number of requirements which make up community-based orders and the proportion of offenders from each group which receive each of the main types of requirement namely: rehabilitation; unpaid work; curfew; and accredited programmes. Graphs will also be used to set out differences between the different order requirements in terms of successful completion. The paper will also present findings from regression analysis which will identify differences after taking account of other factors. The findings will of necessity be based on broad ethnic groups as it is unlikely that there will be sufficient numbers of people within more narrow ethnic groups to meet statistical disclosure criteria.

ConclusionThere is a lack of information on relationships between ethnicity, gender and community sentences. Better understanding of these relationships has been identified as crucial by HM Inspectorate of Probation. This paper provides more information on these relationships enabling policy decisions to be better targeted to provide equality of outcomes.
Evaluating teacher grading during the pandemic: An analysis of how teacher grades in 2020 compared to exam grades during ‘normal times’
Authors: Stratton Tim , Okpala Chifumnanya
Published: September 2023

The aim was to evaluate teacher grades awarded in 2020 after GCSE and A-level exams were cancelled due to the pandemic. We identified if there were changes in the relationships between student, school and subject-level features and grades awarded by teacher judgement in 2020 when compared to pre-pandemic years.

A multi-level modelling analysis was conducted using the GRADE dataset which links data on pupil characteristics with results data and university application data. The model outputs identifying the relationship between student, school and subject characteristics and GCSE and A-level results were compared between 2020 and normal exam years to identify any notable changes.

A further study focussed on whether university application patterns had an influence on teacher judgements at A-level, since teachers may have known students’ application decisions when deciding on grades. For this analysis university tariff and the degree subject applied to by each student were key variables.

Analysis showed that across both GCSEs and A-levels there was an average increase in results by around half a grade in 2020. There was also less variation in grades in 2020. However, the majority of relationships between grades and other features studied had not substantially changed.

Prior attainment was by far the strongest predictor of grades, but this relationship was slightly stronger in 2020 compared to previous years.

Subjects with more non-exam assessment tended to be those with the largest increase in grades, as well as ‘facilitating’ subjects at A level. There was also a decrease in the attainment gap between male and female students at A level.

The follow up study indicated that teachers were not influenced by student’s application decisions when allocating grades.

Findings suggest that teacher grades did not introduce any substantial sources of bias or different patterns of grading in 2020. This research helped to inform policy decisions around national assessment in England in 2021, as well as contributing to the broader understanding of direct grading by teachers.
The dynamics of the nursing and midwifery professions: Initial findings from analysis of longitudinal registrant data
Authors: Atherton Iain , Jamieson Michelle
Published: September 2023

ObjectivesTo ascertain geographical differences in retention of nurses and midwives across the United Kingdom using registrant data.

MethodsThe Nursing and Midwifery Council (NMC) are responsible for holding a register of all nurses and midwives in the United Kingdom. Registrants are required to revalidate every three years. Linking together resulting data creates a longitudinal dataset that follows registrants over time. The NMC is providing anonymised data through the ONS Safe Researcher Service (SRS). Data sharing agreements have been signed off and data is in process of being ingested by ONS. Initial analysis will focus on geographical differences in retention by for nurses by field of practice (adult, mental health, children, and learning disability) and midwifery.

ResultsThere are estimated to be around 750 thousand nurses and midwives currently registered. Processes used to take this work forward will be described including public and stakeholder engagement. Early findings will be presented comparing demographic profiles of the professions and, for nursing, fields of practice in 2018 and 2021. Cox proportional hazard models will enable comparison of geographical differences in retention between England, Scotland, Wales, and Northern Ireland.

ConclusionRegistrant data provides a basis that can inform policy. This is especially important given current challenges with regard to recruitment and retention in the nursing and midwifery professions. Future work will be outlined that will utilise registrant data including linkage to other administrative and census data sources.
Combining commercial assessment products with administrative data to shape education policy: Tracking pupil outcomes during the Covid-19 pandemic
Authors: Andrews Jon
Published: September 2023

Objectives To inform the government’s response to the COVID-19 pandemic by providing an ongoing analysis of the learning loss and recovery experienced by pupils in England after the cancellation of national statutory assessments. Our aim was to use data from a commercial assessment product linked to large administrative datasets.

Methods We constructed a pupil level dataset of assessments in reading and mathematics. The assessments covered multiple time points in the pandemic period and in the years before. We matched this to the National Pupil Database to give a wide range of pupil characteristics information – including eligibility for free school meals, the area they lived in, and information about their school.

By constructing a regression model of pupil outcomes in the pre-pandemic period, we produced a counterfactual of what we would have expected each pupil to achieve had the pandemic not occurred and hence estimate the extent of learning loss.

Results National lockdowns and restrictions to in-person learning were associated with pupils in England making less progress than normal. By the summer of 2021, learning losses in reading amongst primary-aged pupils amounted to around 0.9 months of learning, and learning losses amongst secondary-aged pupils amounted to around 1.8 months. Learning losses in mathematics were even greater, at 2.8 months. We found that the pandemic’s effects were not felt evenly with larger losses for those from economically disadvantaged backgrounds and larger losses in parts of the north and midlands than in London and the south. Indeed, by the start of the 2021/22 academic year, results in reading for primary aged pupils in London and the south-west were broadly in line with pre-pandemic levels.

Conclusion The project demonstrated the power of combining commercial products with administrative datasets to shape policy. The analysis informed the government’s education response to the pandemic including the targeting of additional support to pupils from low-income backgrounds who, as subsequent national tests have confirmed, were disproportionately affected by the pandemic.
We need to talk about data! Transforming the conversation between researchers and the public
Authors: Welpton Richard
Published: September 2023

ObjectivesOur Future Data Services (FDS) work has shown that researchers find it difficult to start conversations with the public about their work and the use of data. This session will explore how data services could effectively facilitate conversations between the public and researchers in the future.

MethodWe know that the public is much more aware that data about them are collected: sometimes through mistakes made, leading to public engagement as an afterthought.

Researchers have told ESRC that they would like to engage with the public; but barriers prevent them from doing so. ESRC would like to run this workshop to find out from researchers what barriers they believe prevent their engagement with the public.

We’ll facilitate face to face discussions as a deep dive to understand what these barriers are, and what resources would help researchers to meaningfully connect with the public in the future.

ResultsThe input collected from researchers at this workshop will help us draft recommendations about how ESRC/UKRI funded data services can in the future support researchers to engage with the public when designing research proposals that use personal data.

These recommendations will likely identify specific funding opportunities that ESRC can target to enable data services to support researchers to engage with the public. For example, resources could be provided through data service website, such as training materials (including crib sheets and videos) and signposting to organisations that can enable them to speak to the public about how they would like to use their data for research.

These recommendations will be published in Spring 2024 and funding opportunities could be made available from 2025 onwards.

ConclusionESRC has heard researchers say they would like to work with the public when designing and undertaking research with personal data in the future. But routine engagement with the public is a hard ask; unless supported through specific programmes, navigating the public engagement remains niche, yet essential.
Optimising data curation pipelines for population-level analytics in secure data environments: Findings from a phenome-wide analysis in the NHS England Secure Data Environment
Authors: Al Arab Marwa , Welshman Zach , Allara Elias , Nolan John , Denholm Rachel
Published: September 2023

ObjectiveSecure data environments (SDE) ensure safe access to large population-level sensitive data. However, computational capacity is limited in these environments, which leads to challenges in the analysis of large population data within the constraints of a complex cloud architecture leveraging multiple software ecosystems. Here we present an efficient pipeline to conduct phenome-wide analyses using electronic health records (EHR) in the NHS England SDE.

MethodsWe accessed deidentified linked EHR from NHS SDE for around 50 million people in England. The exposure is SARS-CoV-2 infection, with outcomes being a phenome-wide atlas of all diseases recorded in EHR data. For computational efficiency, we created three cohorts tables using PySpark within the Databricks environment and a sampling algorithm with inverse probability weights which adds a flag to the dataset to mark the inclusion of a row in the sample of a specific outcome. We will conduct survival analysis using Cox models in RStudio on the samples while adjusting for potential confounders in the main datasets and 15 subgroups.

ResultsSampling with inverse probability weighting produced datasets that are statistically equivalent to the original population data. In terms of computational efficiency, the time needed to sample and read the data for modeling one outcome is 2.3 min compared to ~45 min when trying to read the entire dataset, which could fail due to the 4GB memory limits of in Rstudio within the SDE. This is particularly important in our study since we will be running at least 13,296 models for main and subgroup analysis in the three cohorts. By adding a flag to each data row to indicate its inclusion in a sample, the sampling strategy significantly reduced the storage space required for the outcome table of each sample.

ConclusionPreparing datasets in Databricks and applying sampling can increase the efficiency of big data analysis pipelines within SDE, save storage space, and help in avoiding memory overload caused by using complete datasets for statistical analysis.
Trends in ethnic inequality in child welfare interventions in Wales, 2010 – 2021
Authors: Jing Yongchao , Cheung Sin Yi , Griffiths Lucy , Scourfield Jonathan
Published: September 2023

ObjectivesChildren’s chances of receiving welfare interventions are found to vary by ethnicity in England, but the ethnic pattern in child welfare interventions in Wales over time is unknown. We aim to estimate the scale and trend of ethnic inequalities in intervention rates in Wales over a 12-year period, using population-based linked administrative records.

MethodsWe analyse cross-sectional administrative children’s social care data: the Children in Need (CIN) dataset from 2010 to 2016 and the Children Receiving Care and Support (CRCS) dataset from 2017 to 2021. These data are linked to both the ethnic population data to obtain the ethnic variation in child welfare intervention rates and, using the WIMD, to identify the relative deprivation level in the neighbourhoods from which children entered care. For observations in CIN/CRCS whose ethnicity are missing, we link Census 2011 to obtain the children’s ethnicity to achieve a fuller coverage. For the first time, our analysis also links children’s social care data with the religion variable in the Census data, allowing us to estimate the extent of religious inequalities in child welfare intervention.

ResultsBased on research findings in England on ethnic variation in child welfare intervention, we hypothesise higher intervention rates among Black children and a lower intervention rates among Asian children in Wales, compared to White children, controlling for deprivation status. By extension, we also hypothesise lower intervention rates among Muslim children compared to Christian children.

ConclusionsNo research to date has quantitatively documented the pattern and trend of ethnic inequalities in child welfare intervention in Wales using linked administrative data on CIN/CRCS. Our study is also the first to examine religious inequality in UK child welfare by linking social care data to the Census. Our findings will have significant implications on policy and practice in social work and particularly in children’s social services. We conclude by discussing what future research questions may emerge from the new insight we shed.
A policy impact case study using real world data from Welsh government fuel poverty schemes to inform scheme design
Authors: Morrison-Rees Sian , Lowe Sarah
Published: September 2023

ObjectivesTo reduce fuel poverty in Wales: the Welsh Government developed schemes to provide energy efficiency improvements to lower income households.

To inform scheme design: investigate health impacts by linking scheme data to health records.

Presented objective: to demonstrate how research findings using real world data can impact policy focus.

ApproachThe research was conducted by an independent researcher at Swansea University who co-produced research questions with the Welsh Government Fuel Poverty Policy Team.

A longitudinal dataset was created linking anonymised ‘Warm Homes: Nest’ improvements data to residents’ routine health records in the SAIL Databank at Swansea University. We examined recipient health before and after intervention compared with controls.

A high-level policy briefing and research report were published in the Welsh Government Social Research – Analysis for Policy series.

Findings were used to design and pilot new eligibility criteria to capture low-income individuals with a respiratory, circulatory or mental health condition.

ResultsThis presentation will describe the policy impact pathway from initial discussions with policymakers to real world change, including:

securing ESRC funding for a Knowledge Transfer Fellowship, which included a 2013 data linking demonstration project…

…which allowed funding to be secured for a 2015-18 research project on the impact of improvements on recipient health…

…which published emerging findings in 2016…

…and substantive findings in 2017, showing a significant positive impact of improvements on recipient health…

…which policymakers used to design a pilot to test ways to widen eligibility criteria to include individuals on a low income with a respiratory, circulatory or mental health condition…

…which led to scheme criteria being widened in 2019.

By 2021, 25% of recipients entered the scheme via the ‘health route’.

ConclusionBy delivering research findings generated using linked real world data, and focused on questions co-produced with policymakers, researchers can successfully impact the design and implementation of government policy, thereby improving the lives of people in the real world - in this case, the health of the citizens of Wales.
Generating a Single Session Outcome Measure from Digital Mental Health Platform Footprints Using Natural Language Processing
Authors: Milligan Gregor , Bernard Aynsley , Dowthwaite Liz , Perez Vallejos Elvira , Goulding James
Published: September 2023

Introduction & BackgroundThis work demonstrates the development of an Adult Session Wants and Needs Outcome Measure (Adult SWAN-OM) aimed at supporting service delivery within the digital mental health platform (DMHP), Qwell. Qwell is a DMHP commissioned by the United Kingdom’s National Health Service which provides access to an online community of peers, a team of experienced counsellors, and a cadre of emotional well-being practitioners. The service is anonymous at point of entry and free for users, and provides an extensive, person-centred approach which results in a wide range of user needs. Deriving outcome measures from the platform’s varied counselling sessions, aims to provide both insights into the want and needs of users and underpin improved mental health support.
Objectives & ApproachThe objective of this research is to show how contemporary machine learning methods (Transformer Models and Contextualised Topic Modelling) may be combined with digital footprint data (in the form of seldom explored text data generated on DMHPs) to identify service user wants and needs. Specifically, with automated inference of patient outcomes currently scarce, we focus on the development of outcome measures in the context of ‘single sessions’, applying machine learning methods to extract topics related to the wants and needs of service users on DMHPs.
Relevance to Digital FootprintsThe data used in this study consisted of transcripts between Qwell practitioners and service users (SU’s) (N=874) at conversation level (N=2323), a filter was applied to the dataset to ensure that focus the SUs elicitation of wants and needs fit into the criteria of a single session. Individuals in the final selected cohort (n=192) are not significantly different from the wider Qwell SU population in the study period in terms of age, gender or ethnicity; suggesting that the cohort is representative of the wider target population. This study shows the potential of mental health digital footprints data when providing insight into the wants and needs of DMHP SUs.
Conclusions & ImplicationsVia this analysis of mental health digital footprint data, this work establishes a process for creating a new outcome measure through the computational analysis of transcript data, incorporating insights from clinical experts and individuals with lived experience of engaging with DMHPs with textual data analysis. This methodological approach of Transformer Models and Contextualized Topic Modelling enables the analysis of a considerable volume of data faster than manually reviewing transcripts. We offer suggestions for the refinement of automated methods, in collaboration with direct support and feedback from both clinicians and individuals with lived experience of DMHPs to enable the understanding of wants and needs of service users within DMHPs.
Peculiarities of motibation the personnel of enterprises in the context of deepening internetional cooperation
Authors:   Lviv Polytechnic National University, Kara Natalia , Pauk N. ,   Lviv Polytechnic National University
Published: September 2023

The article provides a definition of the term "motivation" and an overview of the motivation process. Following a summary of every source examined, the following conclusions can be made: By addressing all of an employee's demands, motivation is a method that encourages staff to perform efficiently to accomplish goals. The writers underlined the significance of researching motivational techniques and frameworks and the utilization of contemporary employee motivation theories and strategies in the context of strengthening global cooperation. The writers concentrate on the factors that contribute to staff motivation in the context of global cooperation. According to the authors, a number of factors are crucial to staff motivation in the context of international cooperation. Transformational leadership that motivates and advances employees is a crucial element. Organizational identification, which refers to a fit between a worker's perception of himself and his employer, is another crucial component. Staff motivation can also be significantly influenced by ongoing attention to employee engagement, and a comprehensive plan includes commitment, organizational identification, and motivation. Employee motivation can also be influenced by fundamental organizational procedures including performance reviews, feedback, engagement, and reward programs. Employers can boost employee intrinsic motivation and engagement in accomplishing their objectives for international cooperation by putting these components into practice. Incentives must be offered to employees in order to improve performance. The key recommendations for the area of employee motivation in businesses involved in international activities are identified based on the analysis of the literature. It is crucial to offer a personalized strategy for each employee when it comes to global cooperation. Particularly, it is important to consider diverse cultural, ethical, and religious aspects that could influence employee motivation. Another factor to think about is developing a healthy corporate culture at the company that supports employee growth and motivates them to deliver excellent results. Opportunities for professional advancement, a focus on wellness, and a balance between work and personal life are examples of components of a positive corporate culture.
Methodological regulations on the classification of management information systems
Authors:   Lviv Polytechnic National University, V Lubomyr
Published: September 2023

In this article the interpretation of the terms “automatic informational system” and “managerial informational system” are specified, developed and implemented the methodical elaborations for principles of managerial informational system classification, the correspondence of the category of management information systems to the levels of management in the general model of enterprise management is highlighted, the classification of existing managerial informational system of domestic enterprises was formed.