Involving lived experience experts in translational research
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A Consensus Statement on establishing causality, therapeutic applications and the use of preclinical models in microbiome research
The gut microbiome comprises trillions of microorganisms and profoundly influences human health by modulating metabolism, immune responses and neuronal functions. Disruption in gut microbiome composition is implicated in various inflammatory conditions, metabolic disorders and neurodegenerative diseases. However, determining the underlying mechanisms and establishing cause and effect is extremely difficult. Preclinical models offer crucial insights into the role of the gut microbiome in diseases and help identify potential therapeutic interventions. The Human Microbiome Action Consortium initiated a Delphi survey to assess the utility of preclinical models, including animal and cell-based models, in elucidating the causal role of the gut microbiome in these diseases. The Delphi survey aimed to address the complexity of selecting appropriate preclinical models to investigate disease causality and to study host–microbiome interactions effectively. We adopted a structured approach encompassing a literature review, expert workshops and the Delphi questionnaire to gather insights from a diverse range of stakeholders. Experts were requested to evaluate the strengths, limitations, and suitability of these models in addressing the causal relationship between the gut microbiome and disease pathogenesis. The resulting consensus statements and recommendations provide valuable insights for selecting preclinical models in future studies of gut microbiome-related diseases.
AI can outperform humans in predicting correlations between personality items
We assess the abilities of both specialized deep neural networks, such as PersonalityMap, and general LLMs, including GPT-4o and Claude 3 Opus, in understanding human personality by predicting correlations between personality questionnaire items. All AI models outperform the vast majority of laypeople and academic experts. However, we can improve the accuracy of individual correlation predictions by taking the median prediction per group to produce a “wisdom of the crowds” estimate. Thus, we also compare the median predictions from laypeople, academic experts, GPT-4o/Claude 3 Opus, and PersonalityMap. Based on medians, PersonalityMap and academic experts surpass both LLMs and laypeople on most measures. These results suggest that while advanced LLMs make superior predictions compared to most individual humans, specialized models like PersonalityMap can match even expert group-level performance in domain-specific tasks. This underscores the capabilities of large language models while emphasizing the continued relevance of specialized systems as well as human experts for personality research.
Not all who integrate are academics: zooming in on extra-academic integrative expertise
Solving complex problems requires integrating knowledge and skills from various domains. The importance of cross-domain integration has motivated researchers to study integrative expertise: what knowledge and skills help achieve cross-domain integration? Much of the existing research focuses on the integrative expertise of academic researchers who perform inter- and transdisciplinary research. However, academics are not the only ones facilitating integration. In transdisciplinary research, where academics collaborate with professionals, stakeholders, and policymakers, these extra-academic actors can contribute significantly to cross-domain integration. Moreover, many complex problems are addressed entirely outside of universities. This paper contributes to a broader, more inclusive understanding of integrative expertise by drawing attention to the diversity of extra-academic integrative expertise, providing examples of what this expertise looks like in practice, and reflecting on differences with its academic counterpart. The contributions are based on a case study of integrative expertise in Oosterweel Link, a large urban development project in Antwerp, Belgium.
Exploring the relationships between soundscape quality and public health using a systems thinking approach
Urban soundscapes significantly influence public health, with sound quality affecting well-being and social value. While traditional noise control has emphasized harm reduction, soundscape studies propose that managing sound environments can promote health benefits. This study explores the complex relationships between soundscape quality and public health using a systems thinking approach. In a participatory workshop with 21 experts from fields such as urban planning, environmental psychology, and acoustics, a causal loop diagram (CLD) was developed to illustrate the interactions between soundscape quality and public health variables. The CLD revealed key feedback loops and intervention points, organized around themes of socio-economic impact, environmental justice, biodiversity, and soundscape design. Findings highlight that while soundscape quality can enhance community well-being, increased economic value may drive gentrification, altering the social structure and reducing sound source diversity. Additionally, the role of soundscape quality in biodiversity suggests both co-benefits and ecological risks. This study demonstrates the potential of systems thinking to guide interdisciplinary approaches in soundscape management, identifying strategic pathways to inform future research and policy development for equitable and health-promoting urban environments.
A lived experience perspective on communication challenges in mental health
When did you first suspect you had a mental health condition? Tell us about your mental health experience I first suspected something was amiss when…
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