The World Health Organization (WHO) defines traditional medicine as “the sum total of the knowledge, skill, and practices based on the theories, beliefs, and experiences indigenous to different cultures, whether explicable or not, used in the maintenance of health as well as in the prevention, diagnosis, improvement or treatment of physical and mental illness” [1]. The National Center for Complementary and Integrative Health (NCCIH) defines “complementary medicine” as a medical approach in which “a non-mainstream approach is used together with conventional medicine”, and “integrative medicine” as a medical approach that “brings conventional and complementary approaches together in a coordinated way” [2, 3]. For the purpose of this study, these three terms will be collectively referred to as traditional, complementary, and integrative medicine (TCIM). It is important to highlight that the field of TCIM is inherently dynamic since the individual therapies that constitute TCIM can be added or removed from this categorization and are directly influenced by the medical practices of the country of origin [2, 4, 5, 6]. This means that diverse therapies coexist under this term, with each therapy reflecting its unique cultural influence [7].
Despite inherent regional differences existing between individual TCIMs, this categorization of these therapies is well acknowledged among researchers, practitioners and policy makers, and as of 2018, 98 countries have adopted national policies regarding TCIM within their respective healthcare systems [8, 9, 10]. An analysis of the volume of scholarly outputs within the field of TCIM shows a consistent increase in the number of publications since the 1940s, with upwards of 200 000 articles published in this field as of 2024 [11, 12]. This steady increase may be attributed to improvements in funding and the development of better research strategies within the field of TCIM [13, 14]. Despite this upward trend, a recurring concern among many conventional medicine practitioners and researchers regarding TCIM is the perceived lack of reliable evidence on its effectiveness available in the literature [15, 16, 17]. This concern was raised within the 2019 WHO Global Report on Traditional and Complementary Medicine where 99 member states cited a lack of reliable evidence as their main challenge towards the integration of TCIM within their respective healthcare systems [8]. Therefore, there is a pressing need for increased and improved research methods and evidence in the field of TCIM [18].
When discussing the current state of research in the field of TCIM, it is important to consider the role of metrics in the assessment of research and how they can influence the perception of scholarly outputs. Traditionally, citation-based counts have been used as a proxy for the reliability and impact of scholarly outputs, with popular citation-based metrics including the journal impact factor and the h-index [19]. In academic research, the assumption is that a greater number of citations is representative of the quality of the work conducted as it implies that it has been influential and served as a foundation for other researchers to build upon [20]. While these traditional metrics have been used for decades to compare the impact of competing research, it is important to acknowledge their drawbacks. Citation-based metrics do a poor job of capturing the engagement with research beyond academic audiences as citation counts only represent a fraction of the way published research is used, ignoring factors that might give insight into the engagement of the general public as well [21, 22].
Non-traditional metrics - known as alternative metrics (altmetrics) - can also be used to calculate the impact of scholarly outputs. The emergence of altmetrics is closely connected to the rise of social media as its introduction to society introduced novel avenues for the discussion of research [23]. This change reflected an evolution in the landscape of scholarly communication, and in turn created the demand for a method to quantify the reach of research across broad online audiences [23, 24]. This demand was initially filled by the Public Library of Science (PLOS) through the induction of Article-Level Metrics (ALMs) which are defined as metrics designed to “measure the reach and impact of individual articles using both academic and social sources, highlighting the many ways in which both scientists and the general public are engaging with the research” [25, 26, 27].
Following this, the term “altmetrics” was established in 2010 by Jason Preim, Dario Taraborelli, Paul Groth, and Cameron Neylon in a published work titled “Altmetrics: A Manifesto” [28]. In this manifesto, the authors call for more research on altmetrics and express their potential stating “ultimately, our tools should use the rich semantic data from altmetrics to ask, “how and why?” as well as “how many?”” [25, 28]. Today, there is no universally accepted definition of altmetrics, but many individual descriptions exist online [25]. For example, the University of Pittsburgh defines altmetrics as a method to “measure and monitor the reach and impact of scholarship and research through online interactions”, but this definition does not stress the diversity of the measures of engagement and output types that can be analyzed using altmetrics [29]. In contrast, the definition of altmetrics proposed by the National Information Standards Organization (NISO) through the NISO Alternative Metrics Project stresses this diversity stating that “altmetrics is a broad term that encapsulates the collection of multiple digital indicators related to scholarly work. These indicators are derived from activity and engagement among diverse stakeholders and scholarly outputs in the research ecosystem, including the public sphere” [30].
Currently, altmetrics providers track activity from a range of sources including public policy documents, online reference managers (e.g., Mendeley), social media platforms (e.g., Facebook, X [formerly Twitter], Weibo, Pinterest), post-publication peer review platforms (e.g., Publons), patents, blogs, and multimedia platforms (e.g., Reddit, YouTube, Q&A from Stack Overflow) [31]. The advantage of using such metrics in the assessment of the impact of research is that they provide insight into the engagement of a more diverse audience, and data can be collected at the article level, independent of metrics at the author or journal level [19].
With these capabilities in mind, the objective of this study is to conduct an altmetric analysis of TCIM scholarly outputs to determine the social impact of research in the field and identify patterns in the factors that drive online engagement.