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Advances in the study of perfectionism in sport
Full text linkInterest in perfectionism in sport psychology has steadily increased over the last twenty-five years. The last 10 years in particular has seen a dramatic increase in research dedicated to the topic. As a result, we have learned a great deal about perfectionism in this domain. However, it is also an area of work in which there has been considerable disagreement on key issues; most notably, the degree to which perfectionism is helpful or a hindrance to athletes. A number of new concepts have recently emerged that may help navigate some of the issues that have historically hampered the study of perfectionism: combined and total unique effects, perfectionistic tipping points, and perfectionistic climate. In this short overview some of the latest advances in this area are introduced, explained, and discussed. Each concept offers interesting opportunities for advancing the study of perfectionism in sport. They also each provide avenues for novel research, as well as impetus to revisit previous research and existing data to yield new insights. Most importantly, the concepts offer the promise of taking us closer to our aim of understanding the effects of perfectionism in sport, and better identifying and supporting athletes at risk to its negative effects
Recent Occurrence of Microplastics in Freshwater and Efficiency of Available Treatment Technologies- A Review
Full text linkThis review assesses microplastic occurrence in freshwater systems globally between 2018 and 2024, examining spatial distribution patterns across rivers, lakes, groundwater, and wastewater treatment plants, alongside treatment technology efficiency. Studies were selected following PRISMA guidelines, with inclusion criteria requiring spectroscopic confirmation using ATR-FTIR or Raman spectroscopy and compliance with ISO/TR 21960 and GESAMP quality control protocols. Microplastics were detected across five continents with notable spatial variations: riverine systems showed mean concentrations of 0.5-5 particles/L, lakes exhibited 0.1-2.5 particles/L, whilst groundwater demonstrated significantly lower levels at 0.01-0.5 particles/L. The most prevalent polymers were polyethylene and polypropylene, primarily linked to secondary microplastic formation from consumer packaging degradation and agricultural film, whilst fibres (predominantly polyester and polyamide) originated from textile washing effluents, representing primary microplastic sources. Conventional drinking water treatment plants achieved 85-95% removal efficiency for particles >50 μm but declined to 40-60% for smaller fractions, with analytical limitations persisting below 5 μm. Emerging technologies including photocatalytic degradation demonstrated up to 70% polypropylene removal, though scalability challenges include high energy requirements (2-5 kWh/m³) and potential toxic intermediate formation. Health implications include endocrine disruption, inflammatory responses, and oxidative stress, with nanoplastics (<1 μm) potentially 10-100 times more prevalent than microplastics, though detection capabilities remain critically limited. Legislative frameworks, including the EU Single-Use Plastics Directive, have shown measurable reductions (20-40%) in targeted polymer types, though enforcement gaps and limited scope continue hampering comprehensive pollution control. Standardised international monitoring protocols remain integral for effective contamination assessment
Higgins, L. (Due 2026). Inclusion and excellence in singing and music education. In C. Nardi & J. v. d. Sandt (Eds.), Singing and Inclusion: Promoting social justice in collective singing with children and youth. Routledge.
No full textMachine learning prediction of kangaroo mother care in Sierra Leone: a comparative study of feature selection techniques and classification algorithms
Full text linkBackground
Kangaroo Mother Care (KMC) is a critical intervention for improving neonatal outcomes, particularly for low-birth-weight infants. Identifying predictors of KMC practice remains essential for targeted health interventions and policy development.
Objective
This study utilizes data from the 2019 Sierra Leone demographic and health survey to identify predictors of KMC using different feature selection techniques and classification algorithms.
Methods
We analyzed 7,377 maternal and child health records from the 2019 Sierra Leone demographic and health survey, applying three feature selection techniques and seven classification algorithms. Data preprocessing included class balancing and cross-validation. Three feature selection techniques employed were: Adaptive Ant Colony Optimization (ACO), Recursive Feature Elimination (RFE), and Backward Feature Selection. Seven machine learning algorithms implemented were: Logistic Regression, Support Vector Machine variants, K-Nearest Neighbours, Random Forest, XGBoost, Stacking Ensemble, and Voting Ensemble. Data preprocessing included SMOTE for class imbalance, 5-fold and 10-fold cross-validation, and hyperparameter optimization using GridSearchCV.
Results
Random Forest and XGBoost consistently achieved the highest performance across all feature selection methods. Using consensus features from multiple selection techniques, Random Forest achieved an accuracy of 0.72, F1-score of 0.78, and ROC-AUC of 0.7689, whilst XGBoost demonstrated similar performance (accuracy: 0.72, F1-score: 0.78, ROC-AUC: 0.7685). Backward Feature Selection and ACO outperformed RFE in identifying discriminative features. Ensemble methods showed robust generalization capabilities.
Conclusion
Machine learning models, particularly ensemble methods combined with comprehensive feature selection techniques, demonstrate strong predictive capability for KMC practice, offering valuable insights for maternal and child health interventions in Sierra Leone
Living on top of water: Public attitude toward floating houses in North Jakarta, Indonesia
Full text linkThe South Africa Disputes before Apartheid: The United Nations and Commonwealth Relations, 1946-1952
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“Discourses of a less pleasing nature”: Addison, Steele and Impoliteness After The Spectator
No full textThe synergy of statistical and fuzzy logic approaches in mining patterns from the peer-to-peer lending data
No full textEthical oversight of Artificial Intelligence in Nigerian Healthcare: A qualitative analysis of ethics committee members’ perspectives on integration and regulation
Full text linkBackground
The adoption of artificial intelligence (AI) in healthcare has the potential to improve diagnostic accuracy, streamline processes, and address resource shortages, particularly in low- and middle-income countries (LMICs) like Nigeria. However, challenges related to knowledge, ethics, and regulation hinder its implementation.
Aim
This study aimed to explore ethics committee members’ perspectives on AI integration in healthcare across public teaching hospitals in southwest Nigeria, examining their knowledge, perceived benefits, challenges, and regulatory considerations surrounding AI adoption in healthcare settings.
Methods
A qualitative study design was used, involving semi-structured interviews with 10 ethics committee members from five public teaching hospitals across southwest Nigeria. Thematic analysis was conducted using NVivo software to identify key themes regarding knowledge, benefits, challenges, risks, and regulatory needs associated with AI in healthcare.
Results
Participants acknowledged AI’s potential to improve efficiency and accuracy in healthcare. However, they expressed concerns about limited knowledge and training, financial barriers, and data privacy issues. Ethical concerns included potential AI errors and overreliance on technology. Participants highlighted the need for comprehensive regulatory frameworks and emphasized a collaborative approach to AI regulation, involving multiple stakeholders. Trust in AI was found to be contingent upon demonstrated accuracy and reliability.
Conclusions
While participants recognized the benefits of AI in addressing healthcare challenges, significant knowledge gaps, ethical concerns, and regulatory deficiencies present barriers to AI’s successful implementation. Addressing these challenges through training, investment, and multi-stakeholder regulatory efforts could facilitate the responsible and effective integration of AI into Nigeria’s healthcare sector