Cracking the code: AI's role in mapping evidence syntheses to the United Nations Sustainable Development Goals

Cracking the code: AI's role in mapping evidence syntheses to the United Nations Sustainable Development Goals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cracking the code: AI's role in mapping evidence syntheses to the United Nations Sustainable Development Goals Bianca Pilla, Jennifer Stone, Zoe Jordan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6828258/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Archives of Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Understanding the alignment and contributions of research to the United Nations Sustainable Development Goals (UN SDGs) is essential for guiding global progress toward these critical targets. Several SDG mapping approaches have been proposed and tested by organisations and researchers but have not produced consistent results. With its capacity to analyse vast datasets and identify patterns, AI-powered search functionality has been presented as an innovative mechanism for tracking, analysing and reporting SDG research to assess progress towards targets and facilitate evidence-based decisions. This study aimed to assess the reliability of automated mapping approaches utilised by online research databases in mapping published evidence syntheses to the UN SDG-3, Good Health & Wellbeing. Methods This study mapped systematic and scoping reviews published in JBI Evidence Synthesis to SDG- 3, Good Health and Wellbeing. Four unique raters independently assessed 204 evidence syntheses based on relevance to SDG-3. These four raters included AI in three established databases and a manual ‘human’ assessment. Inter-rater reliability was assessed using Light’s Kappa. Results Concurrence occurred for 52% of publications. Inter-rater reliability indicated ‘minimal agreement’ among the four raters in mapping the 204 evidence syntheses to SDG-3. Discrepancies in the publications mapped to SDG-3 across the four raters may be explained by the different taxonomies used by the databases, different machine learning algorithms, and constantly evolving search strategies. Our results indicate significant room for improvement in achieving greater consensus among approaches, including AI algorithms designed to identify such publications. Conclusion The findings of this study emphasise the need for standardised and transparent SDG mapping methods and tools as the 2030 agenda deadline nears and progress on numerous targets lags. Identifying trustworthy evidence across the global ecosystem is critical, but AI's reliability to contribute to this has yet to be established with confidence. It cautions the scientific community, policymakers, funders and others about the importance of comprehensive evaluation when mapping research to SDGs. SDGs United Nations Sustainable Development Goals Evidence Synthesis Natural Language Processing Machine Learning Artificial Intelligence. Contributions to the literature This is the first study to assess the reliability of AI-based tools for mapping evidence syntheses to the UN Sustainable Development Goals (SDGs) The findings demonstrate the need for standardised and transparent SDG mapping methods to inform evidence-based policy. The study informs researchers, funders, and decision-makers about the limitations of relying on AI systems to identify evidence related to the Sustainable Development Goals. It supports future efforts to improve mapping accuracy by combining automated and expert-led approaches. BACKGROUND Challenges such as climate crises, ecological disasters, cybercrime, political conflict, mass migration, inflation, debt crises in low- and middle-income countries, and a pandemic are testing global responses for health and healthcare systems. This unique and complex global 'polycrisis' threatens the achievement of the United Nations Sustainable Development Goals (UN SDGs) and the well-being of individuals everywhere ( 1 ). Given these intersecting crises and the increasing interconnectedness of health, climate, and societal issues, it is crucial to adopt multisectoral, agile, and adaptable evidence-based approaches to public policy formation and decision-making. The UN agenda includes 17 SDGs associated with 169 targets and 232 progress indicators( 2 ). The goals urge political, scientific, economic, and societal change to address global challenges and ensure sustainable development of the planet. Fulfilling these goals by 2030 will take an unprecedented effort by all sectors of society. International research and health communities are vital in achieving these goals by providing the necessary evidence and innovations to overcome many difficult and complex societal challenges ( 3 ). The limited progress towards the SDGs underscores the importance of developing evidence-based and practical recommendations for the acceleration of these goals. Recognising this need, the United Nations Development Program has called for syntheses of rigorous evidence on what has worked, why and where to advance and accelerate the SDG achievements, including evidence syntheses about which programs, policies and practices are most effective in improving indicators related to the goals ( 4 ). Understanding the alignment and contributions of scientific research to the SDGs is essential for guiding the allocation of resources toward these critical targets ( 5 ) and informing the prioritisation and management of evidence synthesis efforts to ensure the relevance of topics and avoid duplication of effort ( 3 ). There has also been a growing vested interest by universities and research funders in tracking, analysing and showcasing how researchers contribute to achieving these goals and demonstrate global research impact at an institutional level( 6 ). Identifying and categorizing research articles that correspond to specific SDGs allows for a comprehensive understanding of the research activities that support each SDG at various levels, including global, country, institutional, and individual. Moreover, it has direct implications for the search and retrieval of articles used in evidence synthesis, supporting the United Nations Development Program's call for rigorous evidence synthesis. However, mapping research publications to SDGs has been difficult as there is no consensus on the best approach to identify publications and evaluate the scope of such research efforts( 7 ). Approaches to mapping the Sustainable Development Goals Over the past nine years, several SDG mapping approaches have been proposed and tested using systematic methods, bibliometric analysis, and natural language processing (NLP) techniques. However, they have not produced consistent results( 8 , 9 ). For example, in 2015, Elsevier, in collaboration with SciDev.Net, pioneered a systematic approach to mapping sustainability science ( 8 ). Their seminal report series explored the research landscape related to the SDGs. To identify relevant studies, they worked with experts to develop Boolean queries for Scopus Advanced search ( 10 ). Key phrases from report titles and abstracts were used to find scholarly articles in the Scopus database, allowing retrieval of SDG-related papers without directly using the term “sustainable development goal.” In 2017, the Sustainable Development Solutions Networks (SDSN) Australia, New Zealand & Pacific presented a ‘Compiled list of SDG keywords’ for SDG mapping( 11 ). Several universities in Australia, New Zealand, and the Pacific region jointly developed this keyword list. The initial keyword list developed by Monash University was modified with input from SDSN and peer universities to a total of 915 SDG-specific keywords covering all 17 SDGs. In 2020, acknowledging the diversity in approaches to mapping SDG research, Armitage et al.( 8 ) compared two methods—those of Jayabalasingham et al.( 10 ) and their own—and found that mapping against SDG targets varied significantly depending on the method used. They created complex Boolean search strings and tested them against translated Elsevier queries in Web of Science. Their findings showed that only a quarter of records were retrieved by both search methods, highlighting significant differences in results. They concluded that the selection, combination, and structure of search terms, with even minor changes to search strings, can significantly alter the retrieved academic papers, warranting careful interpretation. This variation is consistent with the broad scope of the SDGs and the diverse research paths that contribute to them. In 2022, Purnell ( 7 ) similarly reported significant discrepancies when comparing four different search query methods. Concurrently, numerous classification systems were developed based on machine learning and screening the increasing amount of digitally available data using automated NLP methods. With its capacity to analyse vast datasets and identify patterns, AI-powered search functionality has been presented as an innovative mechanism for tracking, analysing and reporting SDG datasets and facilitating data-driven decisions( 12 ). This has led to a growing trend of employing AI techniques for mapping SDG-related research and activities. When dealing with large volumes of publications, an automatic classification method is desirable to efficiently and accurately facilitate assigning publications to the corresponding goals. Duran-Silva et al.( 13 ) utilised natural language processing to refine an initial set of SDG-related terms derived from the UN's goals, targets, and indicators, applying these enhanced queries to categorise textual records such as publications and projects. In contrast, Jayabalasingham et al.( 10 ) and Wastl et al.( 14 ) employed a mix of query-based strategies and machine learning to pinpoint SDG-related research within the Scopus and Dimensions databases. Bautista-Puig et al.( 15 ) and Goyeneche et al.( 16 ) used SDG-focused queries on clusters of publications in the Web of Science, organised by citation relationships, while the UN Department of Economic and Social Affairs used topic modelling algorithms, a widely used text-mining method in NLP, to build an SDG classifier for their publications( 17 , 18 ). Following these studies and many others demonstrating discrepancies amongst mapping approaches( 8 , 19 – 25 ), the complexity of Elsevier’s pioneering search queries has been updated annually using a multi-layered AI algorithmic and human approach ( 20 , 26 ). Meanwhile, databases like Dimensions have employed machine learning to create their SDG categorisation schemes(27), regularly adding new research category filters for the SDGs( 28 ). Despite these AI advancements, a 2023 review by Raman et al.( 29 ) comparing SDG 3 (Good Health and Well Being)-related research between 2018 and 2022 from Scopus and Dimensions databases still found considerable discrepancies both in the number of publications mapped to SDG 3 and when comparing results at the country, funder, institution, journal, and author level. A pilot study mapping evidence syntheses Despite the proliferation of mapping approaches and studies, there had been no published studies mapping evidence syntheses to SDGs. While there are many evidence gap maps (EGMs)( 30 ) that summarise the density and paucity of available evidence on the effectiveness of interventions and outcomes broadly related to SDGs and identify gaps in the evidence base, only a handful of these have used the SDG framework (goals, targets and/or indicators) in their study design( 31 , 32 ). Moreover, while EGMs often include systematic reviews, they also map primary studies, impact evaluations and other related evidence. In 2022, to address this gap, the author team of this paper conducted a pilot study( 3 ) to map the alignment of publications in JBI Evidence Synthesis to UN SDG-3 and its 13 targets, with a view to conducting a larger scale analysis to assist JBI’s global collaborative evidence network( 33 ), the JBI Collaboration, in engaging with and working toward achieving the SDGs through the conduct of evidence syntheses. Jordan and Pilla adopted a desktop audit approach to map the targets related to SDG-3 against evidence syntheses published in JBI Evidence Synthesis from 2016–2022. This approach involved an independent manual assessment and assignment of syntheses to SDG-3 targets using a keyword search query informed by the Australia/Pacific Sustainable Development Solutions Network’s list of SDG keywords for universities( 11 ). Limitations to the pilot study included only mapping health-related targets associated with SDG-3, despite there being health-related targets prevalent across other SDGs, and the study being limited to one journal (JBI Evidence Synthesis) . As the 2030 SDG Agenda passes its midpoint, scientists and policymakers are increasingly relying on innovative solutions to bridge the remaining gap towards achieving the SDGs. Given the restricted nature of the pilot study, we aimed to expand this mapping exercise beyond JBI to other global collaborative evidence networks producing evidence syntheses. However, we found that the process adopted in the pilot study of manually screening and categorising text according to SDG targets was labour-intensive and difficult to scale. To address this limitation, we sought to explore alternative, automated mapping approaches to effectively scale up the discoverability of relevant, high-quality evidence syntheses addressing the SDGs. OBJECTIVE This study aims to assess the reliability of automated mapping approaches utilised by online research databases in mapping evidence syntheses (publications) to the United Nations Sustainable Development Goals, specifically SDG 3, Health and Wellbeing. METHODS Data This study used the publication dataset from the SDG-3 Mapping Pilot Study by Jordan and Pilla( 3 ), comprising systematic and scoping reviews (n = 204) published in JBI Evidence Synthesis between January 1, 2016, and April 30, 2022. Database searches Scopus, Dimension and Altmetric databases were selected as they are the three research databases that provide functionality (automated algorithms) for searching and filtering publications by SDGs and index JBI Evidence Synthesis . Searches were conducted for each of the 204 evidence syntheses on 1 February 2024, using publication DOIs (Digital Object Identifiers) and the various SDG classification filters and functions across the three databases( 34 – 36 ). Results were recorded in an Excel spreadsheet (See Supplemental Digital Content 1- Codebook). Database mapping Scopus and Dimensions have built their own classification systems to automatically assign SDGs to publications using unique search queries and machine learning algorithms. Altmetric Explorer retrieves all SDG classification data from Dimensions on a daily basis( 35 ). We provide an overview of the mapping methodologies used by Scopus and Dimensions. Scopus employs a multi-layered strategy for mapping research to the SDGs( 36 ). It utilises an advanced text-mining algorithm to scan titles, abstracts, and keywords against a predefined list of SDG-related terms. To enhance accuracy, the automated process is supplemented by manual curation or expert review. Additionally, the journal's subject classification where the article is published is used as an extra measure of relevance to specific SDGs. Citation analysis is also incorporated to assess the paper's impact within the SDG context. Overall, Scopus emphasises precision over recall, meaning that it privileges the minimisation of false negatives (i.e., the erroneous inclusion of publications unrelated to SDGs) rather than false positives (i.e., the erroneous exclusion of publications related to SDGs)( 37 ). Dimensions, on the other hand, uses a blend of natural language processing (NLP) and machine learning (ML) to map research to SDGs( 34 ). NLP extracts key concepts and ideas from the publications, which ML then matches to relevant SDGs. The mapping process in Dimensions is largely automated, allowing for faster processing, though it may result in more false negatives. Unlike Scopus, Dimensions includes a broader range of publication types in its SDG mapping, aiming for greater inclusivity by linking a wider array of research papers to SDGs. Human mapping This study used the results of a Human Mapping Pilot Study previously conducted by the authors (BP and ZJ) as a comparator( 3 ). A desktop audit approach was adopted to map the targets related to SDG-3 against evidence syntheses published in JBI Evidence Synthesis from 2016 to 2022. This approach involved an independent manual assessment and assignment of syntheses to SDG-3 targets using a keyword search query informed by the Australia/Pacific Sustainable Development Solutions Network’s list of SDG keywords for universities( 11 ). Statistical analysis Inter-rater reliability was assessed using Light’s Kappa( 38 ). Four unique raters included the three established databases and a manual ‘human’ assessment conducted during the Pilot Study. Four raters independently assessed the 204 evidence syntheses (subjects) based on relevance to SDG-3. Stata MP (version 17)( 39 ) was used to calculate Cohen’s Kappa in rater pairs (i.e., A-B. A-C, A-D, etc.) and the average of the kappas taken to obtain Light’s Kappa( 40 ). For fully crossed designs with three or more coders, Light( 38 ) suggests computing kappa for all coder pairs and then using the arithmetic mean of these estimates to provide an overall index of agreement. The Light’s Kappa value was interpreted according to established cutoffs established by McHugh ( 41 , 42 ) to determine the level of agreement among the raters. These were: 0-0.20 (no agreement), 0.21–0.39 (minimal agreement), 0.40–0.59 (weak agreement), 0.60–0.79 (moderate agreement), 0.80–0.90 (strong agreement) and 0.91–1.00 (almost perfect agreement). RESULTS Results highlighted significant discrepancies in the publications mapped to SDG-3 by the four raters. Dimensions database mapped the largest number of evidence syntheses to SDG-3 (79), followed by Scopus (71), the human mapping pilot study (59), with Altmetric mapping significantly less (30) (See Table 1). Table 1 Number of publications mapped by raters Rater N= independently mapped (i.e. not by other raters) N= total publications mapped % Overall publications mapped Dimensions 19 79 38.73 Scopus 17 71 34.80 JBI Pilot Study 8 59 28.92 Altmetric 0 30 14.71 Concurrence occurred for 52% of publications (n=14 mapped to SDG-3 and n=92 not mapped, out of 204) by all four raters (See Tables 2 and 3). The highest level of agreement was seen by the Scopus Database and the Pilot Study (76.47%), followed by Altmetric and Dimensions Databases (75.98%), Altmetric and the Pilot Study (75%) and Dimensions and the Pilot Study (71.57%). All other rater groups achieved less than <70% agreement. Table 2 Number of publications mapped/not mapped by rater groups (2-4) Table 3 Rater Group Agreement for Rater Groups (2-4) % Inter-rater reliability Inter-rater agreement averaged across rater pairs. Light's Kappa [ K ] yielded a coefficient of K = 0.34 ( p <.001), indicating ‘minimal agreement’ among the four raters in mapping the 204 evidence syntheses (Table 4). (See Supplemental Digital Content 1 for Codebook). T able 4 Inter-rater reliability for each rater pair Rater Pairs Agreement % Expected Agreement % Kappa Agreement Rating p-z Altmetric/Scopus 68.14 60.73 0.1887 None 0.0009 Altmetric/JBI Pilot 75 64.88 0.2882 Minimal 0 Altmetric/Dimensions 75.98 57.96 0.4287 Weak 0 Scopus/JBI Pilot 76.47 56.41 0.4603 Weak 0 Scopus/Dimensions 68.63 53.43 0.3264 Weak 0 JBI Pilot/Dimensions 71.57 54.75 0.3716 Minimal 0 Total 2.0639 0.34398 Minimal p<.001 DISCUSSION To our knowledge, this is the first study to assess the reliability of different approaches for mapping evidence syntheses to SDGs, testing the inter-rater reliability of databases using automated algorithms and human mapping to align evidence syntheses with SDG-3. The findings underscore the complexity and variability inherent in mapping research publications to the United Nations SDGs, particularly SDG-3 (Good Health and Well-being). The minimal inter-rater reliability observed across the different automated mapping systems and the manual human assessment highlights the challenges faced in achieving consistent results, even when using sophisticated AI-driven algorithms. Variability Across Mapping Approaches In the realm of SDGs classification, several factors contribute to the variability observed in the results across different databases. Key among these factors are the differing taxonomies employed by databases, the variety of machine learning algorithms utilized, and the continually evolving nature of search strategies. While systematic and comprehensive evaluations of the accuracy of these automated classification systems are lacking, the few existing studies indicate some striking differences in their comparative outputs ( 7 , 8 , 25 , 29 ). Raman et al.( 29 ) demonstrated discrepancies between Scopus and Dimensions databases. However, their study was limited by conducting sensitive searches yielding different results across databases rather than mapping an existing dataset for direct comparison. Moreover, as Raman et al. noted, the machine learning algorithms and search strategies of these databases are constantly evolving, warranting frequent investigation( 29 ). Previous studies have highlighted how different taxonomies can lead to inconsistencies in classification outcomes, as each database might interpret SDG indicators differently based on its structure and guidelines( 43 ). Additionally, the choice of machine learning algorithms plays a critical role, as different algorithms are optimized for various tasks and can yield differing results depending on the context in which they are applied( 44 ). It is interesting that Altmetric and Dimension had a low kappa value, given that Altmetric adopts the Dimensions classification system. This suggests that secondary platforms relying on third-party data may introduce further discrepancies. This could be due to the filtering or aggregation processes applied by Altmetric or differences in how the data is integrated and utilized. Altmetric does not, however, detail how they integrate this into their database search filters/functionality. Purnell refers to this as the SDG mapping ‘Black Box’( 7 ), encouraging those designing methods or tools for identifying SDG-related research to publish each element of their method so that end users can choose from a more informed perspective. Scopus and Dimensions, despite both utilizing advanced machine learning and natural language processing techniques, showed only minimal agreement in their classification of SDG-3-related publications. This variation could be attributed to differences in the search queries, algorithms, and underlying data models each platform uses to map publications to SDGs. Scopus, which emphasizes precision and relies on a multi-layered strategy including manual curation, mapped fewer publications than Dimensions, which aims for broader inclusivity but at the potential cost of increased false negatives. These discrepancies are consistent with previous findings that highlight the impact of methodological choices, including keyword selection and query structure, on the results of SDG mapping efforts( 7 , 10 ). Text mining algorithms, which are central to identifying relevant publications, employ a range of methodologies, including frequency-based approaches, statistical models, and machine-learning techniques( 45 ). Each methodology has unique strengths and weaknesses, significantly impacting the outcomes of text classification tasks. The pre-processing steps, such as tokenisation, stemming, and stop word removal, are crucial in preparing text data for analysis; however, they can also introduce variations that lead to inconsistencies in keyword identification( 46 ). These inconsistencies are particularly evident in cases where slight differences in pre-processing can result in substantial changes in algorithmic outputs, as demonstrated by several recent studies in natural language processing( 12 , 45 ). The performance of text mining algorithms is inconsistent across all text data domains, and the subjective nature of human judgment further complicates this variability. Human experts, especially those with domain-specific knowledge, are adept at recognizing contextually relevant keywords that algorithms might overlook. This expertise highlights a significant challenge in developing automated classification systems, which often struggle to capture the nuances of language diversity and cultural contexts( 46 ). Algorithms trained on labelled datasets may not fully represent the breadth of linguistic and cultural variations, leading to biases that affect performance and cause discrepancies in keyword identification compared to human judgment( 47 ). Our findings emphasize the need for greater consensus among databases designed to identify SDG-related publications. As automated SDG classification systems become more widespread, it is imperative to validate the different approaches employed to ensure that their outputs accurately reflect the SDGs. This calls for ongoing research and collaboration to refine these tools, ultimately striving for more consistent and reliable results across databases( 9 ). Challenges of automated mapping systems The low agreement between human assessments from the pilot study and the automated systems also points to the limitations of current AI technologies in capturing the nuances of research relevance to SDG targets. While AI-driven methods are essential for handling large volumes of data, the findings suggest that these methods may still miss contextually important connections that human experts can recognise, particularly in complex or interdisciplinary research areas. This aligns with prior research indicating that text mining algorithms, especially those based on machine learning, may not fully capture the diversity of language and cultural contexts, leading to biases and discrepancies in keyword identification( 13 ). Moreover, pre-processing steps like tokenisation, stemming, and stop-word removal can further introduce variations in keyword selection, affecting the performance of these algorithms across different text data domains( 8 , 9 ) Implications for evidence synthesis and SDG research The discrepancies observed have significant implications for evidence synthesis efforts aligned with the SDGs. If different mapping systems yield divergent sets of relevant publications, the comprehensiveness and accuracy of capturing the full body of evidence syntheses related to the SDGs could be compromised. This is particularly concerning given the United Nations Development Program's emphasis on using rigorous evidence syntheses to inform policy and accelerate progress toward the SDGs( 4 ). Researchers and policymakers relying on these systems for evidence synthesis must be aware of these limitations and consider cross-referencing multiple databases to ensure a more complete and accurate mapping of relevant research. The need for improved validation and standardisation Given the inconsistencies across different platforms, there is a clear need for improved validation and standardisation of SDG mapping approaches. This could involve developing a unified framework or set of guidelines for SDG-related research classification, combining the strengths of both automated and human-driven methods. Collaborative efforts among database providers, academic institutions, and international organisations could lead to more reliable and consistent mapping systems, thereby enhancing the utility of SDG-related research for global policymaking. The discrepancies identified in this study mirror those found in earlier comparisons of SDG mapping methods, where different taxonomies and algorithms produced varying results( 7 , 8 ). The increased use of automated SDG classification systems must be accompanied by rigorous efforts to validate these approaches and ensure that their outputs accurately reflect the SDGs. Limitations and directions for future research All automated mapping approaches only assign classifications at the goal level rather than targets and/or indicators. This is a fundamental limitation when searching for relevant research, as all SDGs have numerous and varying targets and indicators (i.e. 13 targets and 28 indicators for SDG-3, ranging from maternal mortality to road traffic and substance abuse). This means that in addition to selecting filters at the goal level, researchers must also employ specific search queries relevant to the target and/or indicator of interest. Considering the heterogeneous mapping approaches, further assessment of the reliability of different automated approaches is needed. While we were able to assess agreement between raters, comparison to a gold standard (probably human in a high-quality study) would allow researchers and policymakers to select the best tool for identifying the full breadth of evidence on specific sustainability goals. Moreover, this study did not analyse the publications mapped by the three databases to SDG-3, including those independently mapped by databases or those that the Pilot Study did not map, to understand why they were mapped, limitations or shortcomings in keyword sets or any possible biases in algorithms/search queries. A more targeted analysis may be performed by examining specific syntheses from the dataset to try and further unpack the discrepancies in SDG classification between systems. As the 2030 SDG Agenda reaches its midpoint, the growing reliance on innovative solutions by scientists and policymakers highlights the critical need for research to address the challenges associated with achieving the SDGs. This necessitates the development of more refined tools and methodologies for comprehensive mapping and systematic evidence identification, including evidence synthesis, which supports the UN Program. Key implications for research include: Refinement of Algorithmic Assessment Criteria : There is a pressing need to enhance algorithmic assessment criteria and improve training protocols to achieve better alignment in keyword detection. Current minimal agreement and discrepancies often arise due to variations in algorithm parameters, differences in vocabularies, and sensitivity to context. Addressing Terminological Variations : Research should focus on refining algorithms to better understand contextual nuances and domain-specific knowledge. Variations in terminology, synonyms, and contextual information, along with noise in the data, significantly contribute to inconsistencies in keyword extraction. Optimizing pre-processing techniques is essential to address these issues. Development of Ensemble Methods : Research should explore the use of ensemble methods or consensus-based approaches to mitigate disagreements among different algorithms. These methods can enhance the reliability and accuracy of keyword detection by leveraging the strengths of multiple algorithms( 9 ). Integration of Human Expertise : Combining human expertise with algorithmic approaches is crucial. Techniques such as human-in-the-loop or hybrid systems should be further investigated to improve the effectiveness and accuracy of evidence identification processes. This integration can help bridge the gap between algorithmic precision and human judgment, leading to more robust and reliable outcomes. These research directions are essential for advancing the methodologies needed to support the successful implementation of the SDGs as we approach 2030. CONCLUSION As the 2030 deadline for the SDGs approaches, ensuring that research mapping is accurate and reliable will be crucial for driving evidence-based policy decisions and achieving global sustainability goals. The findings of this study emphasise the need for standardised and transparent SDG mapping methods and tools as the 2030 agenda deadline nears and progress on numerous targets lags. Identifying trustworthy evidence across the global ecosystem is critical; however, while automated systems offer a scalable solution for mapping research to SDGs, the observed discrepancies call for ongoing refinement and greater standardisation across platforms. A multi-faceted approach that incorporates both automated and manual processes is needed to improve the reliability of SDG research mapping and ultimately support global efforts to achieve the UN Sustainable Development Goals. It cautions the scientific community, policymakers, funders, and other stakeholders about the importance of comprehensive evaluation when mapping research to SDGs. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests None to declare Funding No funding to declare Author Contribution BP conceived of the presented idea and initiated the project. BP, ZJ and JS developed the method. BP conducted the database searches and mapping. JS conducted the statistical analysis. BP, ZJ and JS contributed to writing the manuscript. Acknowledgements N/A Availability of data and materials The codebook has been supplied as a supplementary file. References Nations U. 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Discrepancies in Mapping Sustainable Development Goal 3 (Good Health and Well-Being) Research: A Comparative Analysis of Scopus and Dimensions Databases. Sustainability. 2023;15(23):16413. Snilstveit B, Vojtkova M, Bhavsar A, Stevenson J, Gaarder M, Evidence. Gap Maps: A tool for promoting evidence informed policy and strategic research agendas. J Clin Epidemiol. 2016;79:120–9. Saran A, White H, Albright K, Adona J. Mega-map of systematic reviews and evidence and gap maps on the interventions to improve child well‐being in low‐and middle‐income countries. Campbell Syst Reviews. 2020;16(4):e1116. Tate C, Wang R, Akaraci S, Burns C, Garcia L, Clarke M, et al. The contribution of urban green and blue spaces to the United Nation's sustainable development goals: an evidence gap map. Cities. 2024;145:104706. Pilla B, Jordan Z, Christian R, Kynoch K, McInerney P, Cooper K, et al. JBI series paper 4: the role of collaborative evidence networks in promoting and supporting evidence-based health care globally: reflections from 25 years across 38 countries. J Clin Epidemiol. 2022;150:210–5. Fane BDH, Wastl J. Using Digital Science's Dimensions database to track research with the UN Sustainable Development Goals. Zenodo. 2022. Smith P. How to: Using the Sustainable Development Goals search filter in Altmetric Explorer [Internet]2022. [cited 2024]. Available from: https://www.altmetric.com/blog/how-to-use-the-sustainable-development-goals-search-filter-in-altmetric-explorer/ Bedard-Vallee A, James C, Roberge G. Elsevier 2023 Sustainable Development Goals (SDGs) Mapping. Elsevier Data Repository https://doi org/10. 2023;17632:y2zyy9vwzy. Bianchini S, Bottero P, Colagrossi M, Damioli G, Ghisetti C, Michoud K. Mapping the Scientific Base for SDGs and Digital Technologies. Luxembourg: Publications Office of the European Union; 2023. Light RJ. Measures of response agreement for qualitative data: some generalizations and alternatives. Psychol Bull. 1971;76(5):365. StataCorp. Stata Statistical Software: Release 17. College Station; 2021. Hallgren KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials Quant methods Psychol. 2012;8(1):23. Landis JR, Koch GG. The measurement of observer agreement for categorical data. biometrics. 1977:159 – 74. McHugh ML. Interrater reliability: the kappa statistic. Biochemia Med. 2012;22(3):276–82. Allen C, Smith M, Rabiee M, Dahmm H. A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals. Sustain Sci. 2021;16(5):1701–16. Sarker IH. Machine learning: Algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160. Iqbal S, Hassan S-U, Aljohani NR, Alelyani S, Nawaz R, Bornmann L. A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies. Scientometrics. 2021;126(8):6551–99. Hovy D, Yang D, editors. The importance of modeling social factors of language: Theory and practice. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human language technologies; 2021. Garrido-Muñoz I, Montejo-Ráez A, Martínez-Santiago F, Ureña-López LA. A survey on bias in deep NLP. Appl Sci. 2021;11(7):3184. Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1Codebook.xlsx Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Archives of Public Health → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 27 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor assigned by journal 23 Jun, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 05 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6828258","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489755855,"identity":"c4241acf-6b4b-4cf8-805f-a91371063835","order_by":0,"name":"Bianca Pilla","email":"data:image/png;base64,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","orcid":"","institution":"University of Adelaide","correspondingAuthor":true,"prefix":"","firstName":"Bianca","middleName":"","lastName":"Pilla","suffix":""},{"id":489755856,"identity":"b0262deb-5da3-4c69-ac08-a3bd63f3a79d","order_by":1,"name":"Jennifer Stone","email":"","orcid":"","institution":"University of Adelaide","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Stone","suffix":""},{"id":489755857,"identity":"ee85d2a1-e2c8-413f-b184-7aee0340caf3","order_by":2,"name":"Zoe Jordan","email":"","orcid":"","institution":"University of Adelaide","correspondingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"","lastName":"Jordan","suffix":""}],"badges":[],"createdAt":"2025-06-05 10:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6828258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6828258/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13690-025-01784-0","type":"published","date":"2025-11-25T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97178542,"identity":"ec79d1b5-28ee-4817-97a3-5722ff67108d","added_by":"auto","created_at":"2025-12-01 16:10:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060942,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6828258/v1/d96ba06c-f0af-4da3-81a0-0ccc2770ba37.pdf"},{"id":87546664,"identity":"837620f6-5d34-47b9-a6b0-b06e8fd48aec","added_by":"auto","created_at":"2025-07-25 05:13:16","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":62241,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1Codebook.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6828258/v1/4ec52a6bdd8d8669dcfef9a3.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cracking the code: AI's role in mapping evidence syntheses to the United Nations Sustainable Development Goals","fulltext":[{"header":"Contributions to the literature ","content":"\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThis is the first study to assess the reliability of AI-based tools for mapping evidence syntheses to the UN Sustainable Development Goals (SDGs)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe findings demonstrate the need for standardised and transparent SDG mapping methods to inform evidence-based policy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe study informs researchers, funders, and decision-makers about the limitations of relying on AI systems to identify evidence related to the Sustainable Development Goals.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIt supports future efforts to improve mapping accuracy by combining automated and expert-led approaches.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"BACKGROUND","content":"\u003cp\u003eChallenges such as climate crises, ecological disasters, cybercrime, political conflict, mass migration, inflation, debt crises in low- and middle-income countries, and a pandemic are testing global responses for health and healthcare systems. This unique and complex global 'polycrisis' threatens the achievement of the United Nations Sustainable Development Goals (UN SDGs) and the well-being of individuals everywhere (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Given these intersecting crises and the increasing interconnectedness of health, climate, and societal issues, it is crucial to adopt multisectoral, agile, and adaptable evidence-based approaches to public policy formation and decision-making.\u003c/p\u003e\u003cp\u003eThe UN agenda includes 17 SDGs associated with 169 targets and 232 progress indicators(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The goals urge political, scientific, economic, and societal change to address global challenges and ensure sustainable development of the planet. Fulfilling these goals by 2030 will take an unprecedented effort by all sectors of society. International research and health communities are vital in achieving these goals by providing the necessary evidence and innovations to overcome many difficult and complex societal challenges (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe limited progress towards the SDGs underscores the importance of developing evidence-based and practical recommendations for the acceleration of these goals. Recognising this need, the United Nations Development Program has called for syntheses of rigorous evidence on what has worked, why and where to advance and accelerate the SDG achievements, including evidence syntheses about which programs, policies and practices are most effective in improving indicators related to the goals (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding the alignment and contributions of scientific research to the SDGs is essential for guiding the allocation of resources toward these critical targets (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and informing the prioritisation and management of evidence synthesis efforts to ensure the relevance of topics and avoid duplication of effort (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). There has also been a growing vested interest by universities and research funders in tracking, analysing and showcasing how researchers contribute to achieving these goals and demonstrate global research impact at an institutional level(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIdentifying and categorizing research articles that correspond to specific SDGs allows for a comprehensive understanding of the research activities that support each SDG at various levels, including global, country, institutional, and individual. Moreover, it has direct implications for the search and retrieval of articles used in evidence synthesis, supporting the United Nations Development Program's call for rigorous evidence synthesis. However, mapping research publications to SDGs has been difficult as there is no consensus on the best approach to identify publications and evaluate the scope of such research efforts(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eApproaches to mapping the Sustainable Development Goals\u003c/h3\u003e\n\u003cp\u003eOver the past nine years, several SDG mapping approaches have been proposed and tested using systematic methods, bibliometric analysis, and natural language processing (NLP) techniques. However, they have not produced consistent results(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). For example, in 2015, Elsevier, in collaboration with SciDev.Net, pioneered a systematic approach to mapping sustainability science (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Their seminal report series explored the research landscape related to the SDGs. To identify relevant studies, they worked with experts to develop Boolean queries for Scopus Advanced search (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Key phrases from report titles and abstracts were used to find scholarly articles in the Scopus database, allowing retrieval of SDG-related papers without directly using the term \u0026ldquo;sustainable development goal.\u0026rdquo; In 2017, the Sustainable Development Solutions Networks (SDSN) Australia, New Zealand \u0026amp; Pacific presented a \u0026lsquo;Compiled list of SDG keywords\u0026rsquo; for SDG mapping(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Several universities in Australia, New Zealand, and the Pacific region jointly developed this keyword list. The initial keyword list developed by Monash University was modified with input from SDSN and peer universities to a total of 915 SDG-specific keywords covering all 17 SDGs.\u003c/p\u003e\u003cp\u003eIn 2020, acknowledging the diversity in approaches to mapping SDG research, Armitage et al.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) compared two methods\u0026mdash;those of Jayabalasingham et al.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and their own\u0026mdash;and found that mapping against SDG targets varied significantly depending on the method used. They created complex Boolean search strings and tested them against translated Elsevier queries in Web of Science. Their findings showed that only a quarter of records were retrieved by both search methods, highlighting significant differences in results. They concluded that the selection, combination, and structure of search terms, with even minor changes to search strings, can significantly alter the retrieved academic papers, warranting careful interpretation. This variation is consistent with the broad scope of the SDGs and the diverse research paths that contribute to them. In 2022, Purnell (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) similarly reported significant discrepancies when comparing four different search query methods.\u003c/p\u003e\u003cp\u003eConcurrently, numerous classification systems were developed based on machine learning and screening the increasing amount of digitally available data using automated NLP methods. With its capacity to analyse vast datasets and identify patterns, AI-powered search functionality has been presented as an innovative mechanism for tracking, analysing and reporting SDG datasets and facilitating data-driven decisions(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This has led to a growing trend of employing AI techniques for mapping SDG-related research and activities. When dealing with large volumes of publications, an automatic classification method is desirable to efficiently and accurately facilitate assigning publications to the corresponding goals.\u003c/p\u003e\u003cp\u003eDuran-Silva et al.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) utilised natural language processing to refine an initial set of SDG-related terms derived from the UN's goals, targets, and indicators, applying these enhanced queries to categorise textual records such as publications and projects. In contrast, Jayabalasingham et al.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and Wastl et al.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) employed a mix of query-based strategies and machine learning to pinpoint SDG-related research within the Scopus and Dimensions databases. Bautista-Puig et al.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and Goyeneche et al.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) used SDG-focused queries on clusters of publications in the Web of Science, organised by citation relationships, while the UN Department of Economic and Social Affairs used topic modelling algorithms, a widely used text-mining method in NLP, to build an SDG classifier for their publications(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFollowing these studies and many others demonstrating discrepancies amongst mapping approaches(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), the complexity of Elsevier\u0026rsquo;s pioneering search queries has been updated annually using a multi-layered AI algorithmic and human approach (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Meanwhile, databases like Dimensions have employed machine learning to create their SDG categorisation schemes(27), regularly adding new research category filters for the SDGs(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these AI advancements, a 2023 review by Raman et al.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) comparing SDG 3 (Good Health and Well Being)-related research between 2018 and 2022 from Scopus and Dimensions databases still found considerable discrepancies both in the number of publications mapped to SDG 3 and when comparing results at the country, funder, institution, journal, and author level.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eA pilot study mapping evidence syntheses\u003c/h2\u003e\u003cp\u003eDespite the proliferation of mapping approaches and studies, there had been no published studies mapping evidence syntheses to SDGs. While there are many evidence gap maps (EGMs)(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) that summarise the density and paucity of available evidence on the effectiveness of interventions and outcomes broadly related to SDGs and identify gaps in the evidence base, only a handful of these have used the SDG framework (goals, targets and/or indicators) in their study design(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Moreover, while EGMs often include systematic reviews, they also map primary studies, impact evaluations and other related evidence.\u003c/p\u003e\u003cp\u003eIn 2022, to address this gap, the author team of this paper conducted a pilot study(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to map the alignment of publications in \u003cem\u003eJBI Evidence Synthesis\u003c/em\u003e to UN SDG-3 and its 13 targets, with a view to conducting a larger scale analysis to assist JBI\u0026rsquo;s global collaborative evidence network(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), the JBI Collaboration, in engaging with and working toward achieving the SDGs through the conduct of evidence syntheses.\u003c/p\u003e\u003cp\u003eJordan and Pilla adopted a desktop audit approach to map the targets related to SDG-3 against evidence syntheses published in \u003cem\u003eJBI Evidence Synthesis\u003c/em\u003e from 2016\u0026ndash;2022. This approach involved an independent manual assessment and assignment of syntheses to SDG-3 targets using a keyword search query informed by the Australia/Pacific Sustainable Development Solutions Network\u0026rsquo;s list of SDG keywords for universities(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Limitations to the pilot study included only mapping health-related targets associated with SDG-3, despite there being health-related targets prevalent across other SDGs, and the study being limited to one journal \u003cem\u003e(JBI Evidence Synthesis)\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eAs the 2030 SDG Agenda passes its midpoint, scientists and policymakers are increasingly relying on innovative solutions to bridge the remaining gap towards achieving the SDGs. Given the restricted nature of the pilot study, we aimed to expand this mapping exercise beyond JBI to other global collaborative evidence networks producing evidence syntheses. However, we found that the process adopted in the pilot study of manually screening and categorising text according to SDG targets was labour-intensive and difficult to scale. To address this limitation, we sought to explore alternative, automated mapping approaches to effectively scale up the discoverability of relevant, high-quality evidence syntheses addressing the SDGs.\u003c/p\u003e\u003c/div\u003e"},{"header":"OBJECTIVE","content":"\u003cp\u003eThis study aims to assess the reliability of automated mapping approaches utilised by online research databases in mapping evidence syntheses (publications) to the United Nations Sustainable Development Goals, specifically SDG 3, Health and Wellbeing.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData\u003c/h2\u003e\u003cp\u003eThis study used the publication dataset from the SDG-3 Mapping Pilot Study by Jordan and Pilla(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), comprising systematic and scoping reviews (n\u0026thinsp;=\u0026thinsp;204) published in \u003cem\u003eJBI Evidence Synthesis\u003c/em\u003e between January 1, 2016, and April 30, 2022.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDatabase searches\u003c/h3\u003e\n\u003cp\u003eScopus, Dimension and Altmetric databases were selected as they are the three research databases that provide functionality (automated algorithms) for searching and filtering publications by SDGs and index \u003cem\u003eJBI Evidence Synthesis\u003c/em\u003e. Searches were conducted for each of the 204 evidence syntheses on 1 February 2024, using publication DOIs (Digital Object Identifiers) and the various SDG classification filters and functions across the three databases(\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Results were recorded in an Excel spreadsheet (See Supplemental Digital Content 1- Codebook).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDatabase mapping\u003c/h2\u003e\u003cp\u003eScopus and Dimensions have built their own classification systems to automatically assign SDGs to publications using unique search queries and machine learning algorithms. Altmetric Explorer retrieves all SDG classification data from Dimensions on a daily basis(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). We provide an overview of the mapping methodologies used by Scopus and Dimensions.\u003c/p\u003e\u003cp\u003eScopus employs a multi-layered strategy for mapping research to the SDGs(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). It utilises an advanced text-mining algorithm to scan titles, abstracts, and keywords against a predefined list of SDG-related terms. To enhance accuracy, the automated process is supplemented by manual curation or expert review. Additionally, the journal's subject classification where the article is published is used as an extra measure of relevance to specific SDGs. Citation analysis is also incorporated to assess the paper's impact within the SDG context. Overall, Scopus emphasises precision over recall, meaning that it privileges the minimisation of false negatives (i.e., the erroneous inclusion of publications unrelated to SDGs) rather than false positives (i.e., the erroneous exclusion of publications related to SDGs)(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDimensions, on the other hand, uses a blend of natural language processing (NLP) and machine learning (ML) to map research to SDGs(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). NLP extracts key concepts and ideas from the publications, which ML then matches to relevant SDGs. The mapping process in Dimensions is largely automated, allowing for faster processing, though it may result in more false negatives. Unlike Scopus, Dimensions includes a broader range of publication types in its SDG mapping, aiming for greater inclusivity by linking a wider array of research papers to SDGs.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHuman mapping\u003c/h3\u003e\n\u003cp\u003eThis study used the results of a Human Mapping Pilot Study previously conducted by the authors (BP and ZJ) as a comparator(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A desktop audit approach was adopted to map the targets related to SDG-3 against evidence syntheses published in \u003cem\u003eJBI Evidence Synthesis\u003c/em\u003e from 2016 to 2022. This approach involved an independent manual assessment and assignment of syntheses to SDG-3 targets using a keyword search query informed by the Australia/Pacific Sustainable Development Solutions Network\u0026rsquo;s list of SDG keywords for universities(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eInter-rater reliability was assessed using Light\u0026rsquo;s Kappa(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Four unique raters included the three established databases and a manual \u0026lsquo;human\u0026rsquo; assessment conducted during the Pilot Study. Four raters independently assessed the 204 evidence syntheses (subjects) based on relevance to SDG-3.\u003c/p\u003e\u003cp\u003eStata MP (version 17)(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) was used to calculate Cohen\u0026rsquo;s Kappa in rater pairs (i.e., A-B. A-C, A-D, etc.) and the average of the kappas taken to obtain Light\u0026rsquo;s Kappa(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). For fully crossed designs with three or more coders, Light(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) suggests computing kappa for all coder pairs and then using the arithmetic mean of these estimates to provide an overall index of agreement.\u003c/p\u003e\u003cp\u003eThe Light\u0026rsquo;s Kappa value was interpreted according to established cutoffs established by McHugh (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) to determine the level of agreement among the raters. These were: 0-0.20 (no agreement), 0.21\u0026ndash;0.39 (minimal agreement), 0.40\u0026ndash;0.59 (weak agreement), 0.60\u0026ndash;0.79 (moderate agreement), 0.80\u0026ndash;0.90 (strong agreement) and 0.91\u0026ndash;1.00 (almost perfect agreement).\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eResults highlighted significant discrepancies in the publications mapped to SDG-3 by the four raters. Dimensions database mapped the largest number of evidence syntheses to SDG-3 (79), followed by Scopus (71), the human mapping pilot study (59), with Altmetric mapping significantly less (30) (See Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Number of publications mapped by raters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRater\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN= independently mapped (i.e. not by other raters)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN= total publications mapped\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Overall publications mapped\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimensions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e38.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScopus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e34.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJBI Pilot Study\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e28.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltmetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e14.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Concurrence occurred for 52% of publications (n=14 mapped to SDG-3 and n=92 not mapped, out of 204) by all four raters (See Tables 2 and 3). The highest level of agreement was seen by the Scopus Database and the Pilot Study (76.47%), followed by Altmetric and Dimensions Databases (75.98%), Altmetric and the Pilot Study (75%) and Dimensions and the Pilot Study (71.57%). All other rater groups achieved less than \u0026lt;70% agreement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Number of publications mapped/not mapped by rater groups (2-4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"603\" height=\"311\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRater Group Agreement for Rater Groups (2-4) %\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"603\" height=\"319\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInter-rater reliability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInter-rater agreement averaged across rater pairs. Light\u0026apos;s Kappa [\u003cem\u003e\u003csub\u003eK\u003c/sub\u003e\u003c/em\u003e] yielded a coefficient of \u003cem\u003e\u003csub\u003eK\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= 0.34 (\u003cem\u003ep\u003c/em\u003e\u0026lt;.001), indicating \u0026lsquo;minimal agreement\u0026rsquo; among the four raters in mapping the 204 evidence syntheses (Table 4). (See Supplemental Digital Content 1 for Codebook).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable 4 Inter-rater reliability for each rater pair\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRater Pairs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAgreement %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eExpected\u0026nbsp;\u003cbr\u003e\u0026nbsp;Agreement %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eKappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAgreement Rating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-z\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltmetric/Scopus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e68.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e60.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltmetric/JBI Pilot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e64.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.2882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMinimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltmetric/Dimensions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e75.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e57.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.4287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eWeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScopus/JBI Pilot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e76.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e56.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.4603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eWeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScopus/Dimensions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e68.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e53.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.3264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eWeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJBI Pilot/Dimensions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e71.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e54.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.3716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMinimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.0639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.34398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMinimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo our knowledge, this is the first study to assess the reliability of different approaches for mapping evidence syntheses to SDGs, testing the inter-rater reliability of databases using automated algorithms and human mapping to align evidence syntheses with SDG-3. The findings underscore the complexity and variability inherent in mapping research publications to the United Nations SDGs, particularly SDG-3 (Good Health and Well-being). The minimal inter-rater reliability observed across the different automated mapping systems and the manual human assessment highlights the challenges faced in achieving consistent results, even when using sophisticated AI-driven algorithms.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eVariability Across Mapping Approaches\u003c/h2\u003e\u003cp\u003eIn the realm of SDGs classification, several factors contribute to the variability observed in the results across different databases. Key among these factors are the differing taxonomies employed by databases, the variety of machine learning algorithms utilized, and the continually evolving nature of search strategies.\u003c/p\u003e\u003cp\u003eWhile systematic and comprehensive evaluations of the accuracy of these automated classification systems are lacking, the few existing studies indicate some striking differences in their comparative outputs (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Raman et al.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) demonstrated discrepancies between Scopus and Dimensions databases. However, their study was limited by conducting sensitive searches yielding different results across databases rather than mapping an existing dataset for direct comparison. Moreover, as Raman et al. noted, the machine learning algorithms and search strategies of these databases are constantly evolving, warranting frequent investigation(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies have highlighted how different taxonomies can lead to inconsistencies in classification outcomes, as each database might interpret SDG indicators differently based on its structure and guidelines(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Additionally, the choice of machine learning algorithms plays a critical role, as different algorithms are optimized for various tasks and can yield differing results depending on the context in which they are applied(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). It is interesting that Altmetric and Dimension had a low kappa value, given that Altmetric adopts the Dimensions classification system. This suggests that secondary platforms relying on third-party data may introduce further discrepancies. This could be due to the filtering or aggregation processes applied by Altmetric or differences in how the data is integrated and utilized. Altmetric does not, however, detail how they integrate this into their database search filters/functionality. Purnell refers to this as the SDG mapping \u0026lsquo;Black Box\u0026rsquo;(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), encouraging those designing methods or tools for identifying SDG-related research to publish each element of their method so that end users can choose from a more informed perspective.\u003c/p\u003e\u003cp\u003eScopus and Dimensions, despite both utilizing advanced machine learning and natural language processing techniques, showed only minimal agreement in their classification of SDG-3-related publications. This variation could be attributed to differences in the search queries, algorithms, and underlying data models each platform uses to map publications to SDGs. Scopus, which emphasizes precision and relies on a multi-layered strategy including manual curation, mapped fewer publications than Dimensions, which aims for broader inclusivity but at the potential cost of increased false negatives. These discrepancies are consistent with previous findings that highlight the impact of methodological choices, including keyword selection and query structure, on the results of SDG mapping efforts(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eText mining algorithms, which are central to identifying relevant publications, employ a range of methodologies, including frequency-based approaches, statistical models, and machine-learning techniques(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Each methodology has unique strengths and weaknesses, significantly impacting the outcomes of text classification tasks. The pre-processing steps, such as tokenisation, stemming, and stop word removal, are crucial in preparing text data for analysis; however, they can also introduce variations that lead to inconsistencies in keyword identification(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). These inconsistencies are particularly evident in cases where slight differences in pre-processing can result in substantial changes in algorithmic outputs, as demonstrated by several recent studies in natural language processing(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe performance of text mining algorithms is inconsistent across all text data domains, and the subjective nature of human judgment further complicates this variability. Human experts, especially those with domain-specific knowledge, are adept at recognizing contextually relevant keywords that algorithms might overlook. This expertise highlights a significant challenge in developing automated classification systems, which often struggle to capture the nuances of language diversity and cultural contexts(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Algorithms trained on labelled datasets may not fully represent the breadth of linguistic and cultural variations, leading to biases that affect performance and cause discrepancies in keyword identification compared to human judgment(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur findings emphasize the need for greater consensus among databases designed to identify SDG-related publications. As automated SDG classification systems become more widespread, it is imperative to validate the different approaches employed to ensure that their outputs accurately reflect the SDGs. This calls for ongoing research and collaboration to refine these tools, ultimately striving for more consistent and reliable results across databases(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eChallenges of automated mapping systems\u003c/h2\u003e\u003cp\u003eThe low agreement between human assessments from the pilot study and the automated systems also points to the limitations of current AI technologies in capturing the nuances of research relevance to SDG targets. While AI-driven methods are essential for handling large volumes of data, the findings suggest that these methods may still miss contextually important connections that human experts can recognise, particularly in complex or interdisciplinary research areas. This aligns with prior research indicating that text mining algorithms, especially those based on machine learning, may not fully capture the diversity of language and cultural contexts, leading to biases and discrepancies in keyword identification(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Moreover, pre-processing steps like tokenisation, stemming, and stop-word removal can further introduce variations in keyword selection, affecting the performance of these algorithms across different text data domains(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImplications for evidence synthesis and SDG research\u003c/h2\u003e\u003cp\u003eThe discrepancies observed have significant implications for evidence synthesis efforts aligned with the SDGs. If different mapping systems yield divergent sets of relevant publications, the comprehensiveness and accuracy of capturing the full body of evidence syntheses related to the SDGs could be compromised. This is particularly concerning given the United Nations Development Program's emphasis on using rigorous evidence syntheses to inform policy and accelerate progress toward the SDGs(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Researchers and policymakers relying on these systems for evidence synthesis must be aware of these limitations and consider cross-referencing multiple databases to ensure a more complete and accurate mapping of relevant research.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eThe need for improved validation and standardisation\u003c/h2\u003e\u003cp\u003eGiven the inconsistencies across different platforms, there is a clear need for improved validation and standardisation of SDG mapping approaches. This could involve developing a unified framework or set of guidelines for SDG-related research classification, combining the strengths of both automated and human-driven methods. Collaborative efforts among database providers, academic institutions, and international organisations could lead to more reliable and consistent mapping systems, thereby enhancing the utility of SDG-related research for global policymaking. The discrepancies identified in this study mirror those found in earlier comparisons of SDG mapping methods, where different taxonomies and algorithms produced varying results(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The increased use of automated SDG classification systems must be accompanied by rigorous efforts to validate these approaches and ensure that their outputs accurately reflect the SDGs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and directions for future research\u003c/h2\u003e\u003cp\u003eAll automated mapping approaches only assign classifications at the goal level rather than targets and/or indicators. This is a fundamental limitation when searching for relevant research, as all SDGs have numerous and varying targets and indicators (i.e. 13 targets and 28 indicators for SDG-3, ranging from maternal mortality to road traffic and substance abuse). This means that in addition to selecting filters at the goal level, researchers must also employ specific search queries relevant to the target and/or indicator of interest.\u003c/p\u003e\u003cp\u003eConsidering the heterogeneous mapping approaches, further assessment of the reliability of different automated approaches is needed. While we were able to assess agreement between raters, comparison to a gold standard (probably human in a high-quality study) would allow researchers and policymakers to select the best tool for identifying the full breadth of evidence on specific sustainability goals.\u003c/p\u003e\u003cp\u003eMoreover, this study did not analyse the publications mapped by the three databases to SDG-3, including those independently mapped by databases or those that the Pilot Study did not map, to understand why they were mapped, limitations or shortcomings in keyword sets or any possible biases in algorithms/search queries. A more targeted analysis may be performed by examining specific syntheses from the dataset to try and further unpack the discrepancies in SDG classification between systems.\u003c/p\u003e\u003cp\u003eAs the 2030 SDG Agenda reaches its midpoint, the growing reliance on innovative solutions by scientists and policymakers highlights the critical need for research to address the challenges associated with achieving the SDGs. This necessitates the development of more refined tools and methodologies for comprehensive mapping and systematic evidence identification, including evidence synthesis, which supports the UN Program.\u003c/p\u003e\u003cp\u003eKey implications for research include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRefinement of Algorithmic Assessment Criteria\u003c/b\u003e: There is a pressing need to enhance algorithmic assessment criteria and improve training protocols to achieve better alignment in keyword detection. Current minimal agreement and discrepancies often arise due to variations in algorithm parameters, differences in vocabularies, and sensitivity to context.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAddressing Terminological Variations\u003c/b\u003e: Research should focus on refining algorithms to better understand contextual nuances and domain-specific knowledge. Variations in terminology, synonyms, and contextual information, along with noise in the data, significantly contribute to inconsistencies in keyword extraction. Optimizing pre-processing techniques is essential to address these issues.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDevelopment of Ensemble Methods\u003c/b\u003e: Research should explore the use of ensemble methods or consensus-based approaches to mitigate disagreements among different algorithms. These methods can enhance the reliability and accuracy of keyword detection by leveraging the strengths of multiple algorithms(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntegration of Human Expertise\u003c/b\u003e: Combining human expertise with algorithmic approaches is crucial. Techniques such as human-in-the-loop or hybrid systems should be further investigated to improve the effectiveness and accuracy of evidence identification processes. This integration can help bridge the gap between algorithmic precision and human judgment, leading to more robust and reliable outcomes.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese research directions are essential for advancing the methodologies needed to support the successful implementation of the SDGs as we approach 2030.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAs the 2030 deadline for the SDGs approaches, ensuring that research mapping is accurate and reliable will be crucial for driving evidence-based policy decisions and achieving global sustainability goals. The findings of this study emphasise the need for standardised and transparent SDG mapping methods and tools as the 2030 agenda deadline nears and progress on numerous targets lags. Identifying trustworthy evidence across the global ecosystem is critical; however, while automated systems offer a scalable solution for mapping research to SDGs, the observed discrepancies call for ongoing refinement and greater standardisation across platforms. A multi-faceted approach that incorporates both automated and manual processes is needed to improve the reliability of SDG research mapping and ultimately support global efforts to achieve the UN Sustainable Development Goals. It cautions the scientific community, policymakers, funders, and other stakeholders about the importance of comprehensive evaluation when mapping research to SDGs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eNone to declare\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding to declare\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBP conceived of the presented idea and initiated the project. BP, ZJ and JS developed the method. BP conducted the database searches and mapping. JS conducted the statistical analysis. BP, ZJ and JS contributed to writing the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eN/A\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe codebook has been supplied as a supplementary file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNations U. The Sustainable Development Goals: Report 2022. UN; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNations U. Transforming our world: The 2030 agenda for sustainable development. UN; 2015. 25 Septemeber 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJordan Z, Pilla B. From agenda to action: JBI Evidence Synthesis and the United Nations Sustainable Development Goals. JBI Evid Synthesis. 2024;22(3):364\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEngelbert M, Ravat Z, Yavuz C, Menon D, Grywatz C, Wolf K. Half-time for SDG Evidence Generation: Insights from the Development Evidence Portal. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, Kang W, Mu J. Mapping research to the sustainable development goals: A contextualised approach. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe la Poza E, Merello P, Barber\u0026aacute; A, Celani A. Universities\u0026rsquo; reporting on SDGs: Using the impact rankings to model and measure their contribution to sustainability. Sustainability. 2021;13(4):2038.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePurnell PJ. A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case study of SDG 13\u0026mdash;Climate Action. Quant Sci Stud. 2022;3(4):976\u0026ndash;1002.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmitage CS, Lorenz M, Mikki S. 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Appl Sci. 2021;11(7):3184.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aoph","sideBox":"Learn more about [Archives of Public Health](http://archpublichealth.biomedcentral.com/)","snPcode":"13690","submissionUrl":"https://submission.nature.com/new-submission/13690/3","title":"Archives of Public Health","twitterHandle":"@Archpubhealth","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SDGs, United Nations Sustainable Development Goals, Evidence Synthesis, Natural Language Processing, Machine Learning, Artificial Intelligence.","lastPublishedDoi":"10.21203/rs.3.rs-6828258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6828258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnderstanding the alignment and contributions of research to the United Nations Sustainable Development Goals (UN SDGs) is essential for guiding global progress toward these critical targets. Several SDG mapping approaches have been proposed and tested by organisations and researchers but have not produced consistent results. With its capacity to analyse vast datasets and identify patterns, AI-powered search functionality has been presented as an innovative mechanism for tracking, analysing and reporting SDG research to assess progress towards targets and facilitate evidence-based decisions. This study aimed to assess the reliability of automated mapping approaches utilised by online research databases in mapping published evidence syntheses to the UN SDG-3, Good Health \u0026amp; Wellbeing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study mapped systematic and scoping reviews published in \u003cem\u003eJBI Evidence Synthesis\u003c/em\u003e to SDG- 3, Good Health and Wellbeing. Four unique raters independently assessed 204 evidence syntheses based on relevance to SDG-3. These four raters included AI in three established databases and a manual \u0026lsquo;human\u0026rsquo; assessment. Inter-rater reliability was assessed using Light\u0026rsquo;s Kappa.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConcurrence occurred for 52% of publications. Inter-rater reliability indicated \u0026lsquo;minimal agreement\u0026rsquo; among the four raters in mapping the 204 evidence syntheses to SDG-3. Discrepancies in the publications mapped to SDG-3 across the four raters may be explained by the different taxonomies used by the databases, different machine learning algorithms, and constantly evolving search strategies. Our results indicate significant room for improvement in achieving greater consensus among approaches, including AI algorithms designed to identify such publications.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings of this study emphasise the need for standardised and transparent SDG mapping methods and tools as the 2030 agenda deadline nears and progress on numerous targets lags. Identifying trustworthy evidence across the global ecosystem is critical, but AI's reliability to contribute to this has yet to be established with confidence. It cautions the scientific community, policymakers, funders and others about the importance of comprehensive evaluation when mapping research to SDGs.\u003c/p\u003e","manuscriptTitle":"Cracking the code: AI's role in mapping evidence syntheses to the United Nations Sustainable Development Goals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 05:13:12","doi":"10.21203/rs.3.rs-6828258/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T08:16:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T15:34:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264777486343330872076079859627866296409","date":"2025-07-27T07:55:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299745017515385603497926511272649300003","date":"2025-07-25T07:20:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T10:17:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192352143450181154536111577806507027203","date":"2025-07-23T09:56:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T06:37:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T04:43:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-23T04:43:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Public Health","date":"2025-06-05T10:23:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aoph","sideBox":"Learn more about [Archives of Public Health](http://archpublichealth.biomedcentral.com/)","snPcode":"13690","submissionUrl":"https://submission.nature.com/new-submission/13690/3","title":"Archives of Public Health","twitterHandle":"@Archpubhealth","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32d697c4-8cce-402b-9234-41f3b2d55edc","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:03:41+00:00","versionOfRecord":{"articleIdentity":"rs-6828258","link":"https://doi.org/10.1186/s13690-025-01784-0","journal":{"identity":"archives-of-public-health","isVorOnly":false,"title":"Archives of Public Health"},"publishedOn":"2025-11-25 15:57:01","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-07-25 05:13:12","video":"","vorDoi":"10.1186/s13690-025-01784-0","vorDoiUrl":"https://doi.org/10.1186/s13690-025-01784-0","workflowStages":[]},"version":"v1","identity":"rs-6828258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6828258","identity":"rs-6828258","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}