Analysis of high‐resolution data offers greater opportunity to understand the nature of data variability, behaviours, trends and to detect small changes. Climate studies often require complete time series data which, in the presence of missing data, means imputation must be undertaken. Research on the imputation of high‐resolution temporal climate time series data is still at an early phase. In this study, multiple approaches to the imputation of missing values were evaluated, including a structural time series model with Kalman smoothing, an autoregressive integrated moving average (ARIMA) model with Kalman smoothing and multiple linear regression. The methods were applied to complete subsets of data from 12 month time series of hourly temperature, humidity and wind speed data from four locations along the coast of Western Australia. Assuming that observations were missing at random, artificial gaps of missing observations were studied using a five‐fold cross‐validation methodology with the proportion of missing data set to 10%. The techniques were compared using the pooled mean absolute error, root mean square error and symmetric mean absolute percentage error. The multiple linear regression model was generally the best model based on the pooled performance indicators, followed by the ARIMA with Kalman smoothing. However, the low error values obtained from each of the approaches suggested that the models competed closely and imputed highly plausible values. To some extent, the performance of the models varied among locations. It can be concluded that the modelling approaches studied have demonstrated suitability in imputing missing data in hourly temperature, humidity and wind speed data and are therefore recommended for application in other fields where high‐resolution data with missing values are common.

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these “omics” studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data‐driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state‐of‐the‐art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

Living with a long-term medical condition is associated with heightened risk for mental health and psychosocial difficulties, but further research is required on this risk for children and adolescents with a rare disease in the educational setting. The aim of this study is to describe parents’ perceptions of the psychosocial impact of rare diseases on their school-aged children in Western Australia. A cross-sectional survey of 41 parents of school-aged children and adolescents diagnosed with a rare disease completed an online questionnaire. Questions related to their perceptions of health-related stigma, bullying, social competencies and mental health difficulties faced by their child. Results showed that stigmatisation was experienced by 75.6% of participants, and almost half (46.4%) reported their child was bullied. In this sample, parents reported high sensory (vision and hearing) abilities, but low to moderate self-care competence in relation to social activities and peer relations. Almost half of the respondents (43.9%) reported mental health difficulties among their children. Children and adolescents with a rare disease have unique psychological and social issues. These findings highlight the need for greater efforts to meet the diverse psychosocial, physical and emotional needs of children diagnosed with a rare disease who attend mainstream schools in Western Australia.

Background: Preeclampsia is a leading cause of foeto-maternal deaths especially in Sub-Saharan Africa. However, the prevalence and risk factors of preeclampsia are scarce in the Central region of Ghana with previous study assessing individual independent risk factors. This study determined the prevalence and algorithm of adverse foeto-maternal risk factors of preeclampsia. Methods: This multi-centre prospective cross-sectional study was conducted from October 2021 to October 2022 at the Mercy Women’s Catholic Hospital and Fynba Health Centre in Central region, Ghana. A total of 1,259 pregnant women were randomly sampled and their sociodemographic, clinical history, obstetrics and labour outcomes were recorded. Logistic regression analysis using SPSS version 26 was performed to identify risk factors of preeclampsia. Results: Of the 1,259 pregnant women, 1174 were finally included in the study. The prevalence of preeclampsia was 8.8% (103/1174). Preeclampsia was common among 20–29 years age group, those who had completed basic education, had informal occupation, multigravida and multiparous. Being primigravida [aOR = 1.95, 95% CI (1.03–3.71), p = 0.042], having previous history of caesarean section [aOR = 4.48, 95% CI (2.89–6.93), p<0.001], foetal growth restriction [aOR = 3.42, 95% CI (1.72–6.77), p<0.001] and birth asphyxia [aOR = 27.14, 95% CI (1.80–409.83), p = 0.017] were the independent risk factors of preeclampsia. Pregnant women exhibiting a combination of primigravida, previous caesarean section and foetal growth restriction were the highest risk for preeclampsia [aOR = 39.42, 95% CI (8.88–175.07, p<0.001] compared to having either two or one of these factors. Conclusion: Preeclampsia is increasing among pregnant women in the Central region of Ghana. Pregnant women being primigravida with foetal growth restriction and previous history of caesarean section are the highest risk population likely to develop preeclampsia with neonates more likely to suffer adverse birth outcome such as birth asphyxia. Targeted preventive measures of preeclampsia should be created for pregnant women co-existing with multiple risk factors.

Background: COVID-19 and its associated restrictions called for innovations in higher education teaching and learning space with many universities resorting to online teaching and alternative assessments. However, little has been done to understand the academic integrity implications in alternative online and non-invigilated assessments.Aim: This study explored the perceptions of higher education students regarding academic integrity in alternative assessments.Methods: Cross-sectional mixed method design following the parallel convergent approach was utilised in this study. A convenience sample of 380 undergraduate and postgraduate nursing and social science students completed an online survey on academic integrity behaviours associated with alternative assessments.Results: High risk (31.7%) of academic misconduct was perceived among young people (18-24 years old). Collusion was common among nursing students (24.5%) and cheating likely to occur in assessments with longer duration—between 2 and 4 hours (18.8%) and between 1 and 2 weeks (46%). Qualitative data resulted in 274 findings and three themes— (i) impossible to cheat; (ii) easy to cheat and (iii) understanding the consequence of cheating. Suggestions for preserving academic integrity in alternative assessments were also made from the qualitative data.Conclusion: Like other forms of traditional assessments, alternative assessments have increased risk of breach of academic integrity; however, with the right strategies, they could serve as effective means of assessing learning outcomes.

Planned behaviour theory was used in a path analysis modelling to investigate the serial mediation role of teaching methods and personality traits (locus of control, need for achievement and entrepreneurial attitude) in the relationship between entrepreneurship curriculum and entrepreneurial intention among university students in Ghana. A proposed 40-item instrument was used to measure outcomes for six constructs (3 personality trait constructs, entrepreneurship curriculum, teaching methods and entrepreneurial intention) for 324 participants. Acceptable convergent, divergent and construct validity scores were observed for the instrument. Teaching methods fully mediated the first-order relationships between entrepreneurial curriculum and each personality traits. The three constructs of personality traits parallelly mediated the second-order relationship between teaching methods and entrepreneurial intention. Teaching methods and each personality trait serially mediated the relationship between entrepreneurial curriculum and entrepreneurial intention. This empirical evidence provides insight into the design of pragmatic interventions by major stakeholders including entrepreneurship educators to inspire students into start-up activities