Current methods to evaluate a journal’s impact rely on the downstream citation mapping used to generate the Impact Factor. This approach is a fragile metric prone to being skewed by outlier values and does not speak to a researcher’s contribution to furthering health outcomes for all populations. Therefore, we propose the implementation of a Diversity Factor to fulfill this need and supplement the current metrics. It is composed of four key elements: dataset properties, author country, author gender and departmental affiliation. Due to the significance of each individual element, they should be assessed independently of each other as opposed to being combined into a simplified score to be optimized. Herein, we discuss the necessity of such metrics, provide a framework to build upon, evaluate the current landscape through the lens of each key element and publish the findings on a freely available website that enables further evaluation. The OpenAlex database was used to extract the metadata of all papers published from 2000 until August 2022, and Natural language processing was used to identify individual elements. Features were then displayed individually on a static dashboard developed using TableauPublic, which is available at www.equitablescience.com. In total, 130,721 papers were identified from 7,462 journals where significant underrepresentation of LMIC and Female authors was demonstrated. These findings are pervasive and show no positive correlation with the Journal’s Impact Factor. The systematic collection of the Diversity Factor concept would allow for more detailed analysis, highlight gaps in knowledge, and reflect confidence in the translation of related research. Conversion of this metric to an active pipeline would account for the fact that how we define those most at risk will change over time and quantify responses to particular initiatives. Therefore, continuous measurement of outcomes across groups and those investigating those outcomes will never lose importance. Moving forward, we encourage further revision and improvement by diverse author groups in order to better refine this concept.
Citation: Gallifant J, Zhang J, Whebell S, Quion J, Escobar B, Gichoya J, et al. (2023) A new tool for evaluating health equity in academic journals; the Diversity Factor. PLOS Glob Public Health 3(8): e0002252. https://doi.org/10.1371/journal.pgph.0002252
Editor: Zahra Zeinali, University of Washington Department of Global Health, UNITED STATES
Received: February 6, 2023; Accepted: July 13, 2023; Published: August 14, 2023
Copyright: © 2023 Gallifant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data have been sourced from open-access sources as described in the methods. These are freely available from the referenced sources without special privileges.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
The last decade has seen our capacity to store, analyse and distribute health data grow exponentially, especially with the growing use of artificial intelligence (AI), yet healthcare has tried and failed to implement it in a successful manner. The current AI landscape is ever-expanding and many of the current models are either still in the prototype stage [1, 2] or exhibit substandard performance, particularly in the cases of sepsis and COVID-19 [3, 4]. More worryingly, AI has inherent bias, introduced by both the data and those who created it, and it is no surprise that it can disproportionately affect minorities [5, 6]. This has led to the call for greater transparency in the model development phase, improved data sharing, and more diversity among research groups to safeguard against such biases . However, these changes have not yet reached the journal-level as the current metrics used to evaluate research and journal impact do not focus on such factors. Furthermore, the ability to provide a complete measure of health research’s significance, penetrance, and relevance has been debated for decades .
Initially designed to track citations of articles by authors and journals, the impact factor (IF) is now used to judge the importance of scientific or academic publications and the journal itself. Though the IF accounts for variations in publishing volumes between journals, the impact on the population or a community had little relevance to the indices [9–12]. Currently, the IF has transformed into a proxy for the quality of individual articles even though highly cited papers skew calculations. As a result, journal IF figures do not represent the majority of papers published within a specific journal [13–16]. In addition, the IF has several limitations, such as not accounting for the citation density of fields or fluctuations in publication practices, for example, during the COVID-19 pandemic, which inflated critical care journals’ impact factors [17, 18]. Moreover, leading academic journals have gamed the system to improve their own IF through self-citation, a practice which is equally common among leading authors [19, 20].
Despite the emphasis on IF, and citations as proxy, they do not equate to scientific excellence or the advancement of health research that improves outcomes for all . There is increasing evidence of disparities in outcomes across demographic groups, where the COVID-19 pandemic was a prime example [22, 23]. These disparities are further reinforced by nonrepresentative research, in regards to the lack of diversity in both researchers and questions . Having a research group that better represents their population in question allows for better coverage of multiple problem-solving styles and a better understanding of the problems they face [25, 26]. Furthermore, increased geographical diversity of authorship has been found to be strongly correlated with scientific impact and can help lead to better science .
Yet, despite the heavy emphasis on the importance of diversity, there is no objective measure for tracking progress towards inclusivity in science or for evaluating who contributes to health research [7, 28]. A shift from a single citation-based metric to using several different metrics that provide a more complete perspective on factors aligned with scientific excellence based on contribution to advancing diversity and inclusion, improving health outcomes, and achieving equity is therefore necessary.
Characteristics of a diversity factor for assessing journal impact
This proposed Diversity Factor (DF) should provide an alternative means of tracking accurate and reliable contributions to health research aligned with the impact on the global community or population in addition to offering an approach to facilitate scientific excellence that is unbiased, representative, and impactful. Important factors that should be considered in evaluating the literature include features related to publications, authorship, and research oversight. These are explored in Table 1 along with guiding questions that describe the feature’s respective characteristics. It is important to note that this is merely a proposal and should serve as a foundation to build upon. Additionally, this proposed Diversity Factor should serve as a supplement to the currently used metrics and not as a replacement. Aggregation, scoring and weighting of each of these features requires rigorous survey multiple diverse stakeholder groups and is planned for future revisions. For this concept paper, each key element is reviewed independent of the others.
1. Dataset characteristics.
Data selection is an inevitable component of research; not all data can be captured and instead strict selection is necessary to answer the research question at hand. However, this inherently creates a restricted view that affects the conclusions drawn, irrespective of domain. The properties of datasets used to develop medical devices and inform clinical decisions are vital as these conclusions will likely have the greatest relevance to the populations studied . The authors propose that datasets used in health research should be mapped to highlight geographic areas of data poverty, expose underlying knowledge gaps, and draw attention to imbalanced datasets . Key properties of datasets that should be monitored include gender imbalance, race-ethnicity, language, age, and geography. The current impact factor rewards citations equally but, determining who has read, utilized, and has been ‘impacted’ by the research is not as simple as implied. Most data being analyzed to guide healthcare is derived from a few centers, almost exclusively based in High-Income Countries . As such, increasing the global impact of research is vital, and the increasing adoption of technology has created the potential to democratize health research. More effort should then be placed on increasing the diversification of the data pool used to design clinical guidelines and develop tools that provide beneficial outcomes for all, and not just a select few countries.
2. Author country.
Author’s previous experiences, surrounding culture, and the associated team will significantly shape projects. Thus, when considering how to evaluate an author group, it is important to consider the diversity, as this will provide insight into what perspectives were considered in questions asked and conclusions reached. Evaluating the spread in the country of affiliations within studies can speak to the cognitive diversity of the teams and the likelihood of methodology and results transferring to that area. The authors designed the study methodology, conducted the analysis, interpreted the findings and presented these in an organized manner. Throughout these stages, biases can be introduced by influencing selection strategies, modes of analyses and presentation of results. Authors working in one country who analyze datasets from another have inherent limitations due to an incomplete understanding of the context and culture of the studied subjects. Including diverse perspectives can maximize the scope for identifying potential biases and ensuring the results produced are applicable to multiple populations. Notably, historical racial prejudices have resulted in disparities in clinical outcomes between demographics and including a variety of backgrounds would improve safeguarding against introducing similar biases . It is commonplace for knowledge to centralize, with intellectual centers producing multitudes of research. These ‘Ivory Towers’ often overrepresent a particular demographic that is inconsistent with the experience or backgrounds of those most burdened by disease. Increasing diversity within author groups, especially within institutions, can help combat this resulting homogeneity of thought.
3. Author gender.
Diversity is more than just increasing the number of ethnicities within the author group. Traditionally, academia has been a male-dominated field, well-documented across multiple fields [33, 34]. However within recent years, this trend has been shifting—more and more women are joining the field. However, gender-parity has not yet been reached and given the current trends will take several more years unless there are active, intentional changes. In this study, an algorithm trained to identify ‘gender’ as Male or Female was utilized. However, as further research is done to refine this proposal, expanding this definition to include other genders and the distinction between sex and gender would be ideal.
4. Organizational or departmental affiliation.
Most health research has traditionally been conducted in a few institutions with the necessary funding and access to data. Considering the centralization of knowledge and the overrepresentation of certain demographics found in these institutions, there has been a recent shift to increase engagement with local stakeholders and wider population engagement. Further, healthcare is increasingly becoming a multi-disciplinary field as a result of the recognition that socioeconomic factors play significant roles in health outcomes. As such, multidisciplinary teams play an important role in bridging professional boundaries and breaking down the barriers of competing cultural and organizational differences, thus rooting academic work in implementable applications. There are currently divides between clinical, academic, and commercial research that often leaves everyone feeling that data is out of reach. Understanding the current breakdown of institutional affiliation, whether it is academic or commercial, is necessary to see if academia is responding to this multidisciplinary call. Expansion of this definition to include the composition of expertise will also be vital, for example, the interaction of machine learning engineers and social scientists in the field of AI.
Analysis of diversity factor in journals with global reach
To evaluate the current diversity factor landscape, we used the OpenAlex database consisting of the metadata of all papers published from 2000 until August 2022 . The entire database was downloaded locally, where metadata, including author name, affiliation, and study abstract, were extracted. Only journals identified by SCImago were included in the analysis.
Dataset Characteristics were not widely named in study abstracts, and due to the lack of freely available full papers, we could not identify this factor reproducibly. The code to implement this feature evaluation has been validated and is freely available for future use . However, due to these limitations, it has been excluded from the analysis portion of this study.
Author affiliations were identified and geocoded using OpenAlex identification of more than 100,000 research producers in the Research Organisation Registry . We geocoded raw affiliation strings for affiliations with no match using a custom Nominatim API . Geographic locations were matched in 88% of author instances. Thereafter, Author Countries were grouped by income status according to the World Bank; Low-Middle Income Country (LMIC) or HIC Country .
Enriched metadata is produced using fine-tuned Natural Language Processing models (BERT-Pubmed) for research classification and entity extraction, as described elsewhere . Affiliation strings derived from this process were then parsed for information on departmental affiliation that were then categorized into commercial and academic organizations.
We identified the author’s gender using several APIs that demonstrate state-of-the-art performance in validation studies on non-English names, including Gender-API and Genderize [40, 41]. Gender matching was conducted using the first name and affiliation country, with 84% of entries matched. The female: male author ratio was calculated for each paper, and then a mean was calculated for each journal and time period.
Features are displayed individually, and a hypothesis of aggregation is discussed below. Descriptive statistics for each of the three included features is displayed in a subset of eight journals that cover the broadest ranges of speciality, Impact Factor, and traditional prestige. A static dashboard using over 7539 journals was then developed using TableauPublic to represent the full diversity factor landscape and to allow for effect modifiers (open-access and funding sources) to be evaluated; this is available at www.equitablescience.com.
Since the year 2000, 130,721 papers have been identified from 7539 journals, a majority of which are from authors based in North America or Central Asia and Europe. In 2021, there were 0.68 and 0.73 authors per paper per region, respectively, compared to under 0.1 in each of Latin America, the Caribbean, South Asia, and Africa. Underrepresentation of female authors is seen throughout. Taking the mean across all journals, in 2000, the median female:male author ratio per publication was 0.31 and has increased over time to 0.78 in 2021. There are a few countries where journals have reached gender parity, such as Portugal (1.13) and Cuba (1.09). However, despite this trend towards greater representation for female authors, it is unclear how many journals, if any, will reach gender-parity in the next five years given the rate of improvement. Those most at risk of being ‘left behind’ are primarily from low-income countries though some HIC countries such as Japan (0.24) and South Korea (0.26) face such obstacles as well.
Authors from low-middle-income countries are sorely underrepresented as well. In 2021, there were over 5 million authors from high-income countries. In comparison, upper-middle-income countries had 1.5 million authors, lower-middle-income countries had 467,323, and low-income countries had 27,080 authors. Taking the mean across all journals, the median LMIC:non-LMIC author ratio per publication has increased from 0.04 in 2000 to 0.20 in 2021 (Fig 1B)
The mean value for A) median Female: Male author ratio on a paper per journal each year from 2000–2021, B) median LMIC: non-LMIC author ratio per journal each year from 2000–2021.
Interestingly, these trends of female author and LMIC author underrepresentation are reversed when looking at a subset of open-access journals. When comparing the top 25 open access journals and non-open access journals by IF, open access journals consistently had higher proportions of female authors and authors from LMICs over the past two decades.
Table 2 summarizes the findings from a selection of well-known journals from 2021. The female:male ratio of authors in this subset mirrors the consistent pattern of female author underrepresentation seen in all journals, with none from this subset averaging over 1. Author representation from LMICs is consistently low as well, with most journals under 1:10. In addition, most authors were from academic or non-commercial organizations, highlighting the lack of multidisciplinary collaboration.
In this paper, the concept of a Diversity Factor was proposed as a supplemental metric of measuring a journal’s contribution to the research landscape, focusing on diversity, equity, inclusion, and impact on the studied community or population. Analysis of the data under the lens of the key elements of a proposed Diversity Factor reveal unsurprising results. Female authors and authors from LMICs are sorely underrepresented. While trends are improving for both demographics, several obstacles still stand in the way. Academia is becoming more and more centralized especially in high-income countries. As it does so, it becomes more difficult to penetrate, especially when considering the trends of self-citation.
The next steps for implementing a diversity factor would include the availability of dataset characteristics, detailed funding sources, patents and downstream policy impact, and citation mapping. This would allow for a better understanding of who is impacted and who is causing the impact. Additionally, refining the use of author country to a place of birth or time spent in a country would account for those in LMICs who emigrate to other institutions, which is not uncommon. Further, the improvement of gender data or NLP algorithms to account for author gender compared to sex would be another step forward. The operationalisation of this tool would rely on interest from journals and researchers in collaborating to permit this data to be published on each journal’s website and centrally for evaluation. The heterogeneity in the findings between these four diversity metrics and between journals likely means that these four values should not be combined into one ‘diversity value’. Instead, they should be evaluated and compared individually as seen in Fig 2, or other visual tools such as in a star diagram.
Additionally, we propose having such values rechecked yearly to offer checkpoints and track progress as new research is published and populations evolve. Routine reporting would further allow for more detailed analysis to be performed, highlight gaps in knowledge, and reflect confidence in the translation of related research. This is particularly true at the health policy level, where it is known that the social determinants of health vary greatly between countries; therefore, clinical decisions and public health decisions should be made based on information more representative of these populations. It is important to acknowledge that there is not one type of bias, nor one group affected by bias, but many types of bias and many groups that can be biased against. Diversity is not a box-ticking exercise but an essential safeguard against potential biases, especially in countries with greater ethnicity, cultural diversity and particular socio-demographic characteristics. Promotion of these features would encourage thoughtful discourse on how study designs and data characteristics can affect different groups so that researchers and organizations can build the right team for the specific project and its risks. Furthermore, integration of the Diversity Factor can encourage collaboration with LMICs that could reshape the knowledge landscape through the dissemination of work partnered with LMICs.
Our goal was to evaluate the current diversity factor landscape to the highest degree permissible by the availability of data and the current standards of metadata. Data set characteristics were not widely available, which did not permit the evaluation of this important feature. However, we have provided the tools to implement this feature if made available in the future. Gender was determined using NLP, which has demonstrated good performance across countries; however, the ground truth is not available for these studies and so cannot be confirmed. SCImago Journal Ranking is used in place of traditional impact factor due to the open source nature and availability of the feature, there is a similarity between the two, but we also acknowledge differences. We recognise these tools are imperfect, but we hope they provide a ‘big picture’ view of the global research landscape and demonstrate what is possible in the future with greater open access to journals and why these metrics could greatly promote the drive for equitable science.
The four elements used in this paper uncovers a bleak reality unseen by citation-based metrics. Academia as it is now, and the healthcare systems that it shapes, cannot equitably and justly provide for all if it is not reflective of the populations in question. And in order to do that, it is necessary to change the way science evaluates its efforts. The current methods of measuring journal impact are far from ideal and fail to provide an estimate of author and dataset diversity. While diversity should not be the only consideration of researchers or journals, it should complement the downstream citation impact. Yet, tracking who participates in the conversations that shape healthcare and where our opinions are being formed should be monitored and evaluated transparently and publicly.
The Diversity Factor is a call to action for improved representation and the encouragement of diverse perspectives in health research to prevent the perpetuation of biases against subgroups and the advancement of scientific excellence that works for all. It reminds journals and authors to assess how thoughts and data reach the manuscript and whether they consider all perspectives, not just those available at hand. Otherwise, we will continue learning and practising medicine in an echo chamber created by the few ivory tower academics with access to the resources and data required to advance the field left in the hands of a select few institutions.
These findings and a more detailed analysis from this paper are available online to permit comparisons across open access, funding sources and specific use cases such as COVID-19 . Moreover, the code is also freely available online in the interest of reproducibility, the addition of other features, and for later conversion to an active pipeline .
We would like to acknowledge the contribution of the OpenAlex database, without which this project would not have been possible.
- 1. Zhang J, Whebell S, Gallifant J, Budhdeo S, Mattie H, Lertvittayakumjorn P, et al. An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research. The Lancet Digital Health. 2022;4: e212–e213. pmid:35337638
- 2. van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med. 2021;47: 750–760. pmid:34089064
- 3. Wong A-KI, Charpignon M, Kim H, Josef C, de Hond AAH, Fojas JJ, et al. Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality. JAMA Netw Open. 2021;4: e2131674–e2131674. pmid:34730820
- 4. Singh K, Valley TS, Tang S, Li BY, Kamran F, Sjoding MW, et al. Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19. Ann Am Thorac Soc. 2021;18: 1129–1137. pmid:33357088
- 5. HealthLeaders. Root of some health disparities may be buried in technology. [cited 30 Nov 2022]. Available: https://www.healthleadersmedia.com/technology/root-some-health-disparities-may-be-buried-technology
- 6. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med. 2018;178: 1544–1547. pmid:30128552
- 7. Oh SS, Galanter J, Thakur N, Pino-Yanes M, Barcelo NE, White MJ, et al. Diversity in Clinical and Biomedical Research: A Promise Yet to Be Fulfilled. PLoS Med. 2015;12: e1001918. pmid:26671224
- 8. Singh Chawla D. What’s wrong with the journal impact factor in 5 graphs. nature index; 2018. Available: https://www.nature.com/nature-index/news-blog/whats-wrong-with-the-jif-in-five-graphs
- 9. Garfield E. Citation Analysis as a Tool in Journal Evaluation. Science. 1972;178: 471–479. pmid:5079701
- 10. Garfield E, Sher I. Genetics Citation Index. Annu Rep Can Inst Sci Tech Inf/Rapp Annu Inst Can Inf Sci Tech. 1963. Available: http://www.garfield.library.upenn.edu/essays/v7p515y1984.pdf
- 11. Brodman E. Choosing Physiology Journals. Bull Med Libr Assoc. 1944;32: 479–483. pmid:16016669
- 12. Garfield E. Citation Analysis as a Tool in Journal Evaluation. Science. 1972;178: 471–479. pmid:5079701
- 13. Bornmann L, Marx W. How good is research really? EMBO Rep. 2013;14: 226–230. pmid:23399654
- 14. Paulus FM, Cruz N, Krach S. The Impact Factor Fallacy. Front Psychol. 2018;9. pmid:30177900
- 15. Bornmann L, Marx W, Gasparyan AY, Kitas GD. Diversity, value and limitations of the journal impact factor and alternative metrics. Rheumatol Int. 2012;32: 1861–1867. pmid:22193219
- 16. Dimitrov JD, Kaveri SV, Bayry J. Metrics: journal’s impact factor skewed by a single paper. Nature. 2010;466: 179–179. pmid:20613817
- 17. Di Girolamo N, Meursinge Reynders R. Characteristics of scientific articles on COVID-19 published during the initial 3 months of the pandemic. Scientometrics. 2020;125: 795–812. pmid:32836530
- 18. Fraser N, Brierley L, Dey G, Polka JK, Pálfy M, Nanni F, et al. The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape. PLoS Biol. 2021;19: e3000959. pmid:33798194
- 19. Mishra S, Fegley BD, Diesner J, Torvik VI. Self-citation is the hallmark of productive authors, of any gender. PLoS One. 2018;13: e0195773. pmid:30256792
- 20. Taşkın Z, Doğan G, Kulczycki E, Zuccala AA. Self-Citation Patterns of Journals Indexed in the Journal Citation Reports. J Informetr. 2021;15: 101221.
- 21. Ray KS, Zurn P, Dworkin JD, Bassett DS, Resnik DB. Citation bias, diversity, and ethics. Account Res. 2022; 1–15. pmid:35938378
- 22. Sze S, Pan D, Nevill CR, Gray LJ, Martin CA, Nazareth J, et al. Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis. eClinicalMedicine. 2020;29. pmid:33200120
- 23. Magesh S, John D, Li WT, Li Y, Mattingly-app A, Jain S, et al. Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic Review and Meta-analysis. JAMA Network Open. 2021;4: e2134147–e2134147. pmid:34762110
- 24. Seastedt KP, Schwab P, O’Brien Z, Wakida E, Herrera K, Marcelo PGF, et al. Global healthcare fairness: We should be sharing more, not less, data. PLOS Digital Health. 2022;1: e0000102. pmid:36812599
- 25. Aminpour P, Schwermer H, Gray S. Do social identity and cognitive diversity correlate in environmental stakeholders? A novel approach to measuring cognitive distance within and between groups. PLOS ONE. 2021;16: e0244907. pmid:34735453
- 26. Meissner P, Wulf T. The effect of cognitive diversity on the illusion of control bias in strategic decisions: An experimental investigation. European Management Journal. 2017;35: 430–439.
- 27. AlShebli BK, Rahwan T, Woon WL. The preeminence of ethnic diversity in scientific collaboration. Nat Commun. 2018;9: 5163. pmid:30514841
- 28. Striving for Diversity in Research Studies. N Engl J Med. 2021;385: 1429–1430. pmid:34516052
- 29. Panch T, Mattie H, Atun R. Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health. 2019;9: 010318. pmid:31788229
- 30. Ibrahim H, Liu X, Zariffa N, Morris AD, Denniston AK. Health data poverty: an assailable barrier to equitable digital health care. The Lancet Digital Health. 2021;3: e260–e265. pmid:33678589
- 31. Smyth B, Trongtrakul K, Haber A, Talbot B, Hawley C, Perkovic V, et al. Inequities in the global representation of sites participating in large, multicentre dialysis trials: a systematic review. BMJ Global Health. 2019;4: e001940. pmid:31799004
- 32. Cerdeña JP, Plaisime MV, Tsai J. From race-based to race-conscious medicine: how anti-racist uprisings call us to act. Lancet. 2020;396: 1125–1128. pmid:33038972
- 33. Vincent J-L, Juffermans NP, Burns KEA, Ranieri VM, Pourzitaki C, Rubulotta F. Addressing gender imbalance in intensive care. Critical Care. 2021;25: 147. pmid:33863353
- 34. The Lack of Diversity in Healthcare on JSTOR. [cited 14 May 2023]. Available: https://www.jstor.org/stable/26894210
- 35. Priem J, Piwowar H, Orr R. OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. 2022; arXiv:2205.01833.
- 36. whizzlab. health_ai_question-answer. 2022 [cited 1 Sep 2022]. Available: https://github.com/whizzlab/health_ai_question_answer
- 37. Research Organization Registry. 2022 [cited 1 Sep 2022]. Available: https://ror.org/
- 38. Nominatim team. Nominatim. 2022 [cited 1 Sep 2022]. Available: https://nominatim.org/
- 39. The World Bank. World Bank Open Data,. 2022 [cited 1 Sep 2022]. Available: https://data.worldbank.org/
- 40. Gender API Team. Gender API. 2022 [cited 1 Sep 2022]. Available: https://gender-api.com/
- 41. Genderize Team. Genderize API. 2022. Available: https://genderize.io/
- 42. Equitable Science. 2022 [cited 1 Nov 2022]. Available: https://equitablescience.com/