Background: Co-creation is an approach that aims to democratize research and bridge the gap between research and practice, but the potential fragmentation of knowledge about co-creation has hindered progress. A comprehensive database of published literature from multidisciplinary sources can address this fragmentation through the integration of diverse perspectives, identification and dissemination of best practices, and increase clarity about co-creation. However, two considerable challenges exist. First, there is uncertainty about co-creation terminology, making it difficult to identify relevant literature. Second, the exponential growth of scientific publications has led to an overwhelming amount of literature that surpasses the human capacity for a comprehensive review. These challenges hinder progress in co-creation research and underscore the need for a novel methodology to consolidate and investigate the literature. Objective: This study aimed to synthesize knowledge about co-creation across various fields through the development and application of an artificial intelligence (AI)–assisted selection process. The ultimate goal of this database was to provide stakeholders interested in co-creation with relevant literature. Methods: We created a novel methodology for establishing a curated database. To accommodate the variation in terminology, we used a broad definition of co-creation that encompassed the essence of existing definitions. To filter out irrelevant information, an AI-assisted selection process was used. In addition, we conducted bibliometric analyses and quality control procedures to assess content and accuracy. Overall, this approach allowed us to develop a robust and reliable database that serves as a valuable resource for stakeholders interested in co-creation. Results: The final version of the database included 13,501 papers, which are indexed in Zenodo and accessible in an open-access downloadable format. The quality assessment revealed that 20.3% (140/688) of the database likely contained irrelevant material, whereas the methodology captured 91% (58/64) of the relevant literature. Participatory and variations of the term co-creation were the most frequent terms in the title and abstracts of included literature. The predominant source journals included health sciences, sustainability, environmental sciences, medical research, and health services research. Conclusions: This study produced a high-quality, open-access database about co-creation. The study demonstrates that it is possible to perform a systematic review selection process on a fragmented concept using human-AI collaboration. Our unified concept of co-creation includes the co-approaches (co-creation, co-design, and co-production), forms of participatory research, and user involvement. Our analysis of authorship, citations, and source landscape highlights the potential lack of collaboration among co-creation researchers and underscores the need for future investigation into the different research methodologies. The database provides a resource for relevant literature and can support rapid literature reviews about co-creation. It also offers clarity about the current co-creation landscape and helps to address barriers that researchers may face when seeking evidence about co-creation.

Background: Co-creation is increasingly recognized for its potential to generate innovative solutions, particularly in addressing complex and wicked problems in public health. Despite this growing recognition, there are no standards or recommendations for method use in co-creation, leading to confusion and inconsistency. While some studies have examined specific methods, a comprehensive overview is lacking, limiting the collective understanding and ability to make informed decisions about the most appropriate methods for different contexts and research objectives. Objective: This study aimed to systematically compile and analyze methods used in co-creation to enhance transparency and deepen understanding of how co-creation is practiced. Methods: To enhance transparency and deepen understanding of how co-creation is practiced, this study systematically inventoried and analyzed methods used in co-creation. We conducted a systematic methods overview, applying 2 parallel processes: one within the peer-reviewed Health CASCADE Co-Creation Database and another within gray literature. An artificial intelligence–assisted recursive search strategy, coupled with a 2-step screening process, ensured that we captured relevant methods. We then extracted method names and conducted textual, comparative, and bibliometric analyses to assess the content, relationship between methods, fields of research, and the methodological underpinnings of the included sources. Results: We examined a total of 2627 academic papers and gray literature sources, with the literature primarily drawn from health sciences, medical research, and health services research. The dominant methodologies identified were co-creation, co-design, coproduction, participatory research methodologies, and public and patient involvement. From these sources, we extracted and analyzed 956 co-creation methods, noting that only 10% (n=97) of the methods overlap between academic and gray literature. Notably, 91.3% (230/252) of the methods in academic literature co-occurred, often involving combinations of multiple qualitative methods. The most frequently used methods in academic literature included surveys, focus groups, photo voice, and group discussion, whereas gray literature highlighted methods such as world café, focus groups, role-playing, and persona. Conclusions: This study presents the first systematic overview of co-creation methods, providing a clear understanding of the diverse methods currently in use. Our findings reveal a significant methodological gap between researchers and practitioners, offering insights into the relative prevalence and combinations of methods. By shedding light on these methods, this study helps bridge the gap and supports researchers in making informed decisions about which methods to apply in their work. Additionally, it offers a foundation for further investigation into method use in co-creation. This systematic investigation is a valuable resource for anyone engaging in co-creation or similar participatory methodologies, helping to navigate the diverse landscape of methods.

This paper introduces a novel framework for addressing domain adaptation challenges in large language models (LLMs), emphasising privacy-preserving synthetic data generation and efficient fine-tuning. The proposed framework employs a multi-stage approach that includes document ingestion, relevance assessment, and automated dataset creation. This process reduces the need for extensive technical expertise while safeguarding data privacy. We evaluate the framework’s performance on domain-specific tasks in fields such as biobanking and public health, demonstrating that models fine-tuned using our method achieve results comparable to larger proprietary models. Crucially, these models maintain their general instruction-following capabilities, even when adapted to specialised domains, as shown through experiments with 7B and 8B parameter LLMs. Key components of the framework include continuous pre-training, supervised fine-tuning (SFT), and reinforcement learning methods such as direct preference optimisation (DPO), which together provide a flexible and configurable solution for deploying LLMs. The framework supports both local models and API-based solutions, making it scalable and accessible. By enabling privacy-preserving, domain-specific adaptation without requiring extensive expertise, this framework represents a significant step forward in the deployment of LLMs for specialised applications. The framework significantly lowers the barrier to domain adaptation for small- and medium-sized enterprises (SMEs), enabling them to utilise the power of LLMs without requiring extensive resources or technical expertise.

Co-creation is recognised as a promising methodology for addressing complex public health issues by leveraging the collective intelligence of multiple stakeholders to develop tailored solutions. However, there is a lack of clarity and significant fragmentation in the concept. This study aims to define the fundamental attributes and guiding principles of co-creation to establish it as a rigorous, evidence-based, and trustworthy methodology. A participatory concept mapping process based on the Differentiae Principle was employed. The study consisted of four stages: 1) systematic extraction of attributes and their definition from traditional scientific and participatory research methodological literature, 2) screening, 3) semantic analysis and clustering into principles using Natural Language Processing and Large Language Models and 4) selection of a final set of attributes and development of principles. The study involved 50 participants with diverse backgrounds within and outside the Health CASCADE Network over 18 months. The study identified 105 candidate attributes. A set of 38 was selected from which 10 key principles were derived (open access, recognised contribution, ethics and legal, transparency, rigorous evaluation, clear problem, plural evidence, critical reflection, diversity, and structured and flexible processes) across three domains: Governance, Scientific and Methodological Rigour, and Collective Intelligence Processes. The attributes and guiding principles provide a comprehensive foundations for evidence-based co-creation, enhancing its trustworthiness, transparency, reliability, and impact in public health and other fields. Future research and practice should use these principles and attributes to refine and implement co-creation effectively.

This paper presents a web service and a deep learning (DL) pipeline that has been developed and implemented as part of the MSCA Health CASCADE project. The purpose of the web service is to provide support, streamline, and enable participatory methods, such as co-creation, in the public health domain. The DL pipeline assists with translation, transcription, speaker diarization, relation extraction, and summarization of audio recordings. This is achieved by removing the need for time-consuming tasks, such as translating and transcribing audio recordings. Additional value is created by extracting implicit relations from the transcribed text and identifying patterns, key themes, and trends. Finally, providing summaries of the transcripts creates a sense of ownership that can improve stakeholder retention in participatory methods.

This study aimed to assess digital technology usage in co-creation for public health, identify practitioners' technology needs and preferences, and determine key factors guiding technology choices. A structured consensus protocol was employed to engage experts with wide-ranging expertise who have applied co-creation in different areas. First, a virtual workshop with 12 experts generated statements on the current use of digital tools and the desired features for co-creation. Next, an online survey gathered additional insights from 14 practitioners. Qualitative content analysis was then conducted to categorise 123 usage statements, 91 wish statements, and 25 choice-factor statements. Finally, a consensus event validated and refined the categorisations. Communication and data handling emerged as central technologies, reflecting the need to overcome language barriers, facilitate engagement, and analyse feedback. Practitioners consistently wished for improved affordability, intuitive interfaces, and inclusive functionalities to support diverse co-creator groups. Ethical and security considerations, including GDPR compliance, were also highlighted, emphasising the importance of trust and data protection. Factors influencing technology choices ranged from cost and ease of implementation to interoperability and overall process support, underscoring the demand for user-friendly, secure, and feature-rich tools. Digital technologies, including artificial intelligence, can significantly enhance co-creation in public health by promoting inclusivity, streamlining collaboration, and supporting evidence-based decision-making. Ensuring affordability, ethical integrity, and practical usability is essential for maximising impact and fostering equitable outcomes in public health co-creation efforts.