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Item Open Access Intersectional disaggregated data practices and leadership interventions for women in higher education: evidence from Timor-Leste(MDPI AG, 2026-05-20)Timor-Leste, Asia’s youngest nation since its independence in 2002, has been making progress in its education sector. However, these gains have not translated into leadership representation as expected, with women remaining significantly underrepresented in senior academic and managerial roles in higher education. While existing studies highlight the potential of intersectional disaggregated data to enhance the visibility of layered inequalities and inform more targeted leadership interventions, its application in Timor-Leste remains at an early stage. This study examines respondents’ perception of barriers and enablers influencing the collection and use of intersectional disaggregated data, and their association with perceived leadership interventions aimed at advancing women in higher education leadership in Timor-Leste. A survey design was employed, with questionnaires administered to purposively selected academic and non-academic staff across selected universities in Timor-Leste. Data were analysed using descriptive and inferential techniques, including the Kruskal–Wallis test, and Spearman’s rank correlation (ρ). The findings suggest that respondents perceive key leadership interventions to include women’s leadership development programmes, mentorship, mental health support, and establishment of dedicated equality and diversity units Respondents also identified key enablers and barriers influencing the collection and use of intersectional disaggregated data, including staff training in ethical data practices, the use of mixed-method approaches, and the provision of privacy protections, alongside constraints related to data systems, capacity, and leadership support. Spearman’s analysis showed significant associations between perceived enablers and barriers influencing the collection and use of intersectional disaggregated data and perceived leadership interventions. This study contributes to the gender equity literature by providing empirical insights on perceived institutional conditions, reported barriers, enablers and perceived mechanisms through which intersectional data may inform leadership-related interventions in the context of Timor-Leste’s higher education system.Item Open Access The state of the application of brownfield sites for housing and infrastructure development: a systematic review(MDPI AG, 2026-06-08)The necessity of brownfield remediation has drawn more attention to scholarly literature in recent years. This review aims to understand the application of brownfield regeneration to sustainable housing and infrastructure development. With an emphasis on deciding the obstacles to regeneration, success factors, and their effects on outcomes, including the environmental, social, and economic aspects of housing and infrastructure development, this paper employs a systematic literature review approach to analyse current perspectives on brownfield regeneration for housing and infrastructure. This paper employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach and frequency based meta-synthesis to systematically quantify how often specific teams, variables, barriers, or concepts appear in body of literature. This investigation thoroughly examined 88 publications from the Scopus database. The research also classified and highlighted institutional and legal, financial and economic, socio-political, and environmental challenges to brownfield regeneration. The main success criteria for brownfield regeneration are mature policy alignment, approval processes, grants, and access to financial incentives. The developing gap found in this study should be the focus of future research. To address financial obstacles, a strong business case model for brownfield sites that provide investors or stakeholders with a financial forecast is needed. Finally, it is essential to develop a sustainable framework for brownfield site regeneration that encompasses all sustainable dimensions related to housing and infrastructure development. Finally, the review contributes to theory and practice by giving a comprehensive overview of brownfield barriers, success factors, and their impact on academics and industry.Item Open Access Match exposure significantly influences acceleration–speed profile outcomes in elite football(MDPI AG, 2026-07-05)Despite the growing use of acceleration–speed (AS) profiling in elite football, the number and composition of sessions required to generate stable in situ profiles remain unclear. AS profiling provides estimates of maximal theoretical acceleration (A0) and maximal theoretical velocity (S0), which may offer practically relevant information for monitoring player sprint-related qualities. This study examined the influence of profiling-window length and match exposure on in situ AS profile outcomes in 19 professional football players competing in the 2023–2024 English Football League Championship. Profiles were generated using two non-overlapping conditions comprising five consecutive sessions (5SS) and ten consecutive sessions (10SS). Mean A0 values were 6.91 ± 0.36 m/s2 for 5SS and 7.12 ± 0.40 m/s2 for 10SS, while mean S0 values were 9.52 ± 0.29 m/s and 9.89 ± 0.28 m/s, respectively. Reliability was assessed using intraclass correlation coefficients (ICCs), standard error of measurement (SEM), and smallest worthwhile change (SWC). The match count within each profiling window was associated with A0 and S0 outcomes in both 5SS and 10SS conditions (all p ≤ 0.004). However, ICC values were low, particularly for S0, and SEM exceeded SWC across conditions, indicating limited sensitivity for detecting small meaningful changes. These findings suggest that longer profiling windows may provide slightly more stable A0 estimates, whereas S0 appears more sensitive to match exposure and contextual variability. The results highlight the importance of interpreting AS profiles at an individual level and accounting for match exposure when comparing profile outcomes across monitoring windows.Item Embargo Which abstract stylistic features fool ChatGPT research evaluations?(Springer Nature, 2026-12-31)Large Language Models (LLMs) have the potential to be used to support research evaluation and have a moderate capability to estimate the research quality of a journal article from its title and abstract. This paper assesses whether there are abstract textual features unrelated to research quality that may influence ChatGPT’s scores. Using a dataset of 99,277 journal articles submitted to the UK-wide Research Excellence Framework (REF) 2021 assessments, we calculated several readability indicators from abstracts and correlated them with ChatGPT scores and departmental REF scores (all fields) and individual article scores (health and life sciences). From the results, linguistic complexity and length correlated positively with both ChatGPT scores and a departmental average proxy for REF expert scores in most broad fields (Units of Assessment). They were also more strongly associated with ChatGPT research quality scores than with the proxy for REF expert scores in many subject areas, although this may have been due to the influence of the averaging process in the proxy for some. In the health and life sciences, where individual article scores were available, syllables per word and words per sentence had stronger correlations with individual ChatGPT scores than with individual article quality scores. Although cause-and-effect was not tested, these results suggest that ChatGPT may be more likely than human experts to reward linguistic complexity, with a potential bias towards longer and less readable abstracts in many fields. The apparent preference of LLMs for complex language is an undesirable feature for practical applications of LLMs for research quality evaluation, unless solutions can be found.Item Open Access Bayesian thinking in the intensive care unit: from statistical theory to clinical practice(Jaypee Brothers Medical Publishers (P) Ltd, 2026-06-23)Critical care medicine operates in an environment of profound uncertainty, where clinicians must make high-stakes decisions based on incomplete, evolving, and often conflicting information. Despite this, most critical care research is frequentist-based, relying on static thresholds, dichotomous interpretations of evidence, and delayed incorporation of new data. This paradigm may not fully align with the dynamic and probabilistic nature of critical illness. Bayesian approaches offer an alternative framework that explicitly incorporates prior knowledge, continuously updates probabilities as new data emerge, and supports real-time, individualized decision-making. Rather than asking whether an intervention "works" in a binary sense, Bayesian methods estimate the probability of benefit or harm in a given clinical context, thereby aligning more closely with bedside reasoning. Importantly, such approaches are no longer theoretical. Adaptive platform trials have demonstrated the feasibility of Bayesian methodologies at scale, enabling continuous learning, dynamic treatment allocation, and simultaneous evaluation of multiple interventions. In this viewpoint, we explore how Bayesian decision-making could extend beyond research into routine intensive care practice. We discuss its potential to enhance clinical judgment, personalize therapy, and integrate heterogeneous data streams into coherent probabilistic estimates. The question is no longer whether Bayesian methods can be implemented, but how quickly and effectively they can be embedded into everyday critical care practice.
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