Understanding Risks for Maternal Mortality in Rural Bangladesh Using XGBoost, Random Forest, and Decision Tree ML Models

This paper explores the application of machine learning models for predicting pregnancy risks, focusing on the performance comparison of XGBoost, Random Forest, and Decision Tree classifiers. The motivation behind this research stems from the critical need for early identification of high-risk pregnancies to improve maternal health outcomes. Using a dataset consisting of anonymous information from pregnant women in rural Bangladesh, this study implements feature scaling, standardization, and encoding to prepare the data. Both pre- and posthyperparameter tuning results are analysed, with additional focus on handling imbalanced data through the application of SMOTE (Synthetic Minority Oversampling Technique). The evaluation metrics include accuracy, precision, recall, F1-score, and ROC curves for each class. Key findings indicate that XGBoost outperforms the other models, particularly after hyperparameter tuning and SMOTE application, achieving an accuracy of 82%. The study emphasizes the importance of advanced machine learning techniques in healthcare, o↵ering significant implications for early and accurate prediction of pregnancy-related risks

Using Behavioural Skills Training with Healthcare Staff to Promote Greater Opportunities for Independence for People Living with Dementia: A Randomised Single-Case Experimental Design (Preprint)

Approximately 72% of older adults in residential care have dementia and present with different levels of functioning. People living with dementia (PLwD) may not always be facilitated to independently carry out activities of daily living (ADLs) in care, increasing the likelihood of excess disability. This study incorporated behavioural skills training (BST) to train healthcare staff how to increase opportunities for independence for PLwD by using task analyses and least to most (L-M) prompting procedures during ADLs. Three healthcare staff, two female and one male (mean age = 42.67, SD = 16.82), participated in the intervention. The What Works Clearinghouse (WWC) Single-Case Design Technical Documentation guided the study’s design. A randomised single-case experimental (n-of-1) design was employed, using a multiple-baseline design (MBD) across participants (n=3) for three separate ADLs. The dependent variable (DV) was the percentage of correct staff responses when implementing the L-M prompting procedure for each step during ADLs. Visual and statistical analysis demonstrated an increase in correct use of a task analysis and L-M prompting for all three participants during intervention compared to baseline, for ADL1: assistance to stand (effect sizes, d=5.39; d=9.38; and d=6.79); ADL2: assistance with drinking (effect sizes, d=3.27; d=8.55; and d=3.67); and ADL3 assistance to brush teeth (effect sizes, d=5.99; d=12.93;and d=9.39). Maintenance data ranged from 70% to 100% correct responses at follow-up (Mean=93.11% SD=7.85). Participants successfully generalised skills learned to two new ADLs (PLwD eating a meal and putting on a jumper). BST was demonstrated as an effective training strategy to increase opportunities for independent responding for PLwD in care environments. The influencing contingencies on staff behaviour require attention within the healthcare environment

Cognitive Stimulation Therapy (CST): Exploring Perspectives of Trained Practitioners on the Barriers and Facilitators to the Implementation of CST for People Living with Dementia

Dementia is recognised as a disability under the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD). People with disabilities like dementia have the right to access specialised health and social care services, including interventions that support independence and community participation. Cognitive Stimulation Therapy (CST) is an evidence-based psychosocial intervention that improves cognition, communication, confidence, and quality of life for people living with dementia, but an implementation gap means that CST is often not available. This study examines whether trained CST practitioners implemented CST, their perceptions of the acceptability and efficacy of CST, whether the perceived acceptability and efficacy of CST predicted implementation, and practitioners’ opinions on the barriers and facilitators to CST implementation. A mixed-methods approach was used, with 62 participants (91.9% female). Although 95% of participants were trained to deliver CST, 45.2% did not facilitate CST groups. Statistical analysis showed that perceived efficacy significantly predicted both the likelihood of running CST groups (p = 0.006) and the number of groups delivered (p = 0.01). Thematic analysis of qualitative data identified the three key themes of ‘resources’, ‘awareness and education’, and ‘acceptability of CST’. Overall, the results show that while CST is acceptable and deemed highly effective, resources and staffing often impede implementation. The results are discussed in the context of prioritising the rights of people with disabilities and recommendations are made on improving access to evidence-based support

Women and leadership in investment management: exploring organizational and cultural barriers and boundaries

Purpose – Set within the investment management sector in Ireland, this paper aims to examine the cultural and organizational barriers faced by women. We identify social closure theory along with Bourdieu’s “field” and “habitus” constructs and his non-material forms of capital ideology as a collective lens from which to understand women’s “otherness”. Design/methodology/approach – An interpretivist philosophical stance underpins the research. Using a snowball sampling technique, nineteen semi-structured interviews were undertaken with sixteen women and three men, all of whom work, or had worked, within the Irish investment management sector. Findings – A complex mix of cultural and organizational barriers prevents women attaining cultural, social and symbolic capital and “fitting in”. Gendered cultural expectations are pervasive and exclude women from senior roles. The acquisition of capital is deeply reliant on organizational context and is unavailable to women in the same way as men. The boys’ club, presenteeism, performance ethos and workplace structures and practices together act as boundary drawing tactics to exclude women and reinforce how capital in the sector is used as a mechanism for maintaining men’s privilege and positioning women as “other”. Originality/value – Highlighting how closure regimes in investment management both emerge and persist provides essential insights into how gender regimes are maintained. By placing attention not on the individual, but rather on the “field” and “habitus” with its inherent norms and structures, we reveal the implicit and explicit challenges women face. Emphasizing these issues has important implications in prohibiting the perpetration of heteronormative assumptions about women as leaders

The impact of foreign players in the English premier league: a mathematical analysis

We undertake extensive analysis of English Premier League data over the period 2009/10 to 2017/18 to identify and rank key factors affecting the economic and footballing performances of the teams. Alternative end-of-season league tables are generated by re-ranking the teams based on five different descriptors—total expenditure, total funds spent on players, total funds spent on foreign players, the ratio of foreign to British players and the overall profit. The unequal distribution of resources and expenditure between the clubs is analyzed through Lorenz curves. A comparative analysis of the differences between the alternative tables and the conventional end-of-season league table establishes the most likely factors to influence the performances of the teams that we also rank using Principal Component Analysis. We find that the top teams in the league are also those that tend to have the highest expenditure overall, for all players, including foreign players; they also have the highest ratios of foreign to British players. Our statistical and machine learning study also indicates that successful performance on the field may not guarantee healthy profits at the end of the season

Efficient Privacy-Preserving Convolutional Neural Networks with CKKS-RNS for Encrypted Image Classification

The rise of security concerns in cloud-shared infrastructures has introduced significant challenges for maintaining privacy in data processing. Although standard encryption methods provide robust protection for data at rest and during transmission, vulnerabilities arise when data must be decrypted for processing, exposing sensitive raw information to potential privacy risks. This issue is particularly pronounced in sectors governed by stringent regulatory requirements, such as healthcare, genomics, smart government, and finance, among many others, where protecting confidential data is critical. Homomorphic Encryption (HE) cryptosystems are solutions to address privacy concerns by providing encrypted data computations. HE allows a non-trustworthy third-party resource to process encrypted information without disclosure. However, the main challenge toward deploying lattice-based HE schemes in Convolutional Neural Network (CNN) models lies in overcoming the high computational costs associated with these cryptosystems. Efficient cryptographically compatible methods become imperative for designing a privacy-preserving CNN with HE (CNN-HE). This paper proposes a method to improve the performance of CNN-HE using the Residual Number System (RNS)-based Cheon-Kim-Kim Song (CKKS) HE scheme, which enables approximate arithmetic over encrypted real numbers. The CNN-HE with CKKS-RNS enables encrypted inputs to be decomposed into several parts and propagated homomorphically and independently in parallel across the model. The RNS representation enables parallel processing in our models, significantly reducing processing time. Experimental analysis on the MNIST optical character recognition benchmark dataset demonstrates that the proposed CNN-HE-RNS models reduce classification latency concerning state-of-the-art CNN-HE solutions without compromising security and accuracy

Typological and cumulative approaches to risk and adversity in Child and Adolescent Mental Health Services (CAMHS): Retrospective cohort analysis in South London

Background: Childhood adversity is robustly associated with mental ill-health. Yet questions remain about how different ways of conceptualising adversity relate to psychiatric diagnoses and service activity. This research aims to examine associations between typological and cumulative conceptualisations of adversity, and psychiatric diagnosis and service activity. Methods: We analysed risk assessment data from 21,072 young people attending mental health services in South London. These assessments include items relating to maltreatment, parental mental health difficulties, substance misuse, self-harm, and violent behaviour. Using latent class analysis, we identified the following risk typologies: ‘Maltreatment and externalising behaviours’ (n = 971, 4·6 %), ‘Maltreatment but low risk to self and others’ (n = 2526, 12·0 %), ‘Anti-social behaviour’ (n = 2669, 12·7 %), ‘Inadequate caregiver supervision and risk to self and others’ (n = 907, 4·3 %), ‘Risk to self but not to others’ (n = 1725, 8·2 %), and ‘Mental health needs but low risk to self and others’ (n = 12,274, 58·2 %). Two cumulative risk models were created: 1) all risk items 2) Adverse Childhood Experiences-related cumulative risk (ACES-CR). Controlling for gender, ethnicity, age, and deprivation, we examined associations between risk typologies, cumulative risk, and the following outcomes: 1) psychiatric diagnosis 2) face-to-face appointments 3) missed appointments 4) referral to social services. Outcomes: Risk in its various conceptualisations was consistently and robustly associated with conduct disorder. Risk also tended to be associated with more face-to-face appointments, missed appointments, and referral to social services. Associations between individual risk typologies and psychiatric diagnosis and service activity are discussed. Interpretation: Our findings suggest that typological and cumulative approaches to risk and adversity can produce unique insights about diagnostic practices and service activity. This work provides further evidence for the contribution of contextual factors to mental ill-health and further work is required to explore the longer-term trajectories of these young people

Cybersecurity Micro-credentials and Career Path Design: The Digital4Security Good Practices

Cybersecurity is critical to safeguarding digital economies, yet the sector faces a significant expert shortage. Addressing this gap requires scalable and flexible education to upskill both specialists and nonspecialists. This paper introduces a novel, good-practice methodology for the design of micro-credentials and an AI-driven career path planning solution, both aligned with the European Cybersecurity Skills Framework (ECSF). The primary objective is to support the scalable development of standardized, ECSF-aligned cybersecurity micro-credentials that address evolving labour market needs and facilitate personalized career progression. The proposed approach is validated through the Digital4Security case study, where 17 ECSF-aligned micro-credentials were developed and analyzed. Additionally, a dedicated open-source web application, the Cybersecurity Career Path Designer, supports personalized pathway planning for users by matching existing skills to ECSF profiles. This work demonstrates a practical and scalable framework for aligning education with cybersecurity market needs

Interventions to increase uptake in a fecal-immunochemical test population-based colorectal cancer screening program: A quasi-experimental study of first-time invitees

Background: Many countries have established organized colorectal cancer screening programs because they can reduce mortality and incidence from the disease; however, they rely on high participation rates, which are often suboptimal. This study examined the effectiveness of two reminder interventions on uptake rates in Ireland’s population-based BowelScreen program. Method: Employing a quasi-experimental design, one intervention mailed the fecal-immunochemical test (FIT) directly to clients not responding to an initial invitation; the other mailed a reminder letter modified with behavioral insights. Interventions were tested separately and in combination and compared to the standard reminder letter (1: standard reminder letter [SRL]; 2: modified reminder letter [MRL]; 3: SRL + FIT direct [FITD]; and 4: MRL + FITD). Primary outcome: overall uptake rate (test completion at 5 months); Subgroup outcome: uptake rate among only those receiving reminders. Outcomes were modeled using multivariable logistic regression with group allocation as a fixed effect, adjusted for sex and deprivation. Results: Uptake was significantly higher in the FITD groups (SRL: 48%; MRL: 50%; SRL + FITD: 54%; MRL + FITD: 54%; p < .001). After adjustment, compared to the SRL group, FITD groups had significantly higher odds of uptake (MRL: odds ratio [OR], 1.09; 95% confidence interval [CI], 0.96–1.23; SRL + FITD: OR, 1.30; 95% CI, 1.14–1.48; MRL + FITD: OR, 1.26; 95% CI, 1.11–1.44). This was also the case for subgroup analysis. The MRL did not result in higher uptake compared to SRL. Conclusion: Mailing the FIT kit directly to nonresponders resulted in improved FIT uptake. Organized FIT-based screening programs not reaching uptake targets should consider implementing this strategy if not already in place