An assessment of infant medication administration and storage practices in selected communities in the Vhembe District of Limpopo Province, South Africa

Background: Effective infant medication administration and storage is a major public health challenge outlined by the World Health Organization. These challenges may be exacerbated in rural or limited-resource areas. Aim: The aim of this study was to investigate infant medication administration and storage practices. Setting: This study took place in selected communities in the Vhembe District of Limpopo Province, South Africa. Method: Data was collected through 39 semi-structured interviews with infant caretakers and rural health workers. Interviews were recorded when permission was given by participants. Interviews were transcribed and coded using grounded theory and Tesch’s model of data analysis. Themes were agreed upon through consensus discussions with the researchers and an independent coder. Results: Six themes that affect current infant medication administration and storage practices in the Vhembe District were identified: access to infant healthcare, the role of health workers, the devices used in the administration of infant medication, reluctance of the infant to take the medication, storage and reuse of infant medication in the rural home and hygiene practices surrounding infant medication administration. Conclusions: Many factors were found to affect infant medication administration and storage practices in in the Vhembe District. Substantial evidence was found to suggest that the relationship between rural health workers and infant caretakers strongly influences these practices: a great amount of reliance and trust is placed in the health worker. Ensuring proper dosage of infant medication in the rural household arose as a main concern of participants. Reuse of medication in the home and home hygiene practices surrounding infant medication administration are areas of potential future research. This future research may further inform recommendations for infant medication administration and storage practices in the Vhembe District

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Trees, Refining, and Combinatorial Characteristics

The analysis of trees and the study of cardinal characteristics are both of historical and contemporary importance to set theory. In this thesis we consider each of these topics as well as questions relating to (almost) disjoint refinements. We show how structural information about trees and other similar objects is revealed by investigating the determinacy of certain two player games played on them. The games we investigate have classical analogues and can be used to prove structural dichotomies and related results. We also use them to find generalizations of the topological notions of perfectness and scatteredness for spaces like $2^{\kappa}$ and $P_{\kappa}\lambda$ and form connections to when a submodel is e.g. ``$T$-guessing" for a certain tree $T$. Questions surrounding generalizations of the cardinal characteristics $\mathfrak{t}$ (the tower number), $\mathfrak{h}$ (the distributivity number), and $\textbf{non}(\mathcal{M})$ (the uniformity number for category) in particular are considered. For example, we ask whether or not $\mathfrak{h}(\kappa)$ can be defined in a reasonable way. We give several impediments. Generalizations of a combinatorial characterization of $\textbf{non}(\mathcal{M})$ in terms of countably matching families of functions become central for our investigation, and we show how characteristics relating to these generalizations can be manipulated by forcing. Similarly, the question of in which contexts can outer models can add strongly disjoint functions is considered. While Larson has shown \cite{Larson2007} that this is possible with a proper forcing at $\omega_1$, and it is a corollary of a result of Abraham and Shelah \cite{AbrahamShelah1986} that it is consistently impossible at $\omega_2$, we note with Radin forcing that if $\kappa$ has a sufficient amount of measurable reflection, then it can be done at $\kappa$. Turning to the theory of disjoint refinements, we generalize a recent result of Brendle \cite{Soukup2008}, and independently Balcar and Paz\'{a}k \cite{BalcarPazak2010}, that any time a real is added in an extension, the set of ground model reals can be almost disjointly refined to the setting of adding subsets of $\kappa$, and consider related topics

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,500 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Humanity's Last Exam

International audienceBenchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Humanity's Last Exam

International audienceBenchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Humanity's Last Exam

International audienceBenchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai