Adding evidence of the effects of treatments into relevant Wikipedia pages: a randomised trial

Abstract

ObjectivesTo investigate the effects of adding high-grade quantitative evidence of outcomes of treatments into relevant Wikipedia pages on further information-seeking behaviour by the use of routinely collected data.SettingWikipedia, Cochrane summary pages and the Cochrane Library.DesignRandomised trial.ParticipantsWikipedia pages which were highly relevant to up-to-date Cochrane Schizophrenia systematic reviews that contained a Summary of Findings table.InterventionsEligible Wikipedia pages in the intervention group were seeded with tables of best evidence of the effects of care and hyperlinks to the source Cochrane review. Eligible Wikipedia pages in the control group were left unchanged.Main outcome measuresRoutinely collected data on access to the full text and summary web page (after 12 months).ResultsWe randomised 70 Wikipedia pages (100% follow-up). Six of the 35 Wikipedia pages in the intervention group had the tabular format deleted during the study but all pages continued to report the same data within the text. There was no evidence of effect on either of the coprimary outcomes: full-text access adjusted ratio of geometric means 1.30, 95% CI: 0.71 to 2.38; page views 1.14, 95% CI: 0.6 to 2.13. Results were similar for all other outcomes, with exception of Altmetric score for which there was some evidence of clear effect (1.36, 95% CI: 1.05 to 1.78).ConclusionsThe pursuit of fair balance within Wikipedia healthcare pages is impressive and its reach unsurpassed. For every person who sought and clicked the reference on the ‘intervention’ Wikipedia page to seek more information (the primary outcome), many more are likely to have been informed by the page alone. Enriching Wikipedia content is, potentially, a powerful way to improve health literacy and it is possible to test the effects of seeding pages with evidence. This trial should be replicated, expanded and developed.Trial registration numberIRCT2017070330407N2.

How the Field of Infectious Diseases Can Leverage Digital Strategy and Social Media Use During a Pandemic

“…For researchers and authors, crowdsourcing and digital dissemination can lead to greater impact [ 13–17 ]. Since early 2020, biomedical literature on COVID-19 has matured rapidly, with an unprecedented number of scholarly articles [ 18 ].…”

COVID-19 research in Wikipedia

“…Wikipedia articles are created, improved and maintained by the efforts of the community of volunteer editors [46, 10], and they are used in a variety of ways by a wide user base [52, 31, 44]. The information Wikipedia contains is generally considered to be of high-quality and up-to-date [46, 24, 18, 29, 45, 5, 53], notwithstanding margins for improvement and the need for constant knowledge maintenance [10, 32, 16].…”

“…Wikipedia is particularly relevant as a means to access medical information online [30, 20, 53, 55]. Wikipedia medical contents are of very high quality on average [5] and are primarily written by a core group of medical professionals part of the nonprofit Wikipedia Medicine [50]. Articles part of the WikiProject Medicine “are longer, possess a greater density of external links, and are visited more often than other articles on Wikipedia” [33].…”

“…Wikipedia is particularly important as go-point to access trusted medical information [55, 53]. Fortunately, Wikipedia biomedical articles have been repeatedly found to be highly visible and of high quality [5, 33]. Wikipedia’s verifiability policy mandates that readers can check the sources of information contained in Wikipedia, and that reliable sources should be secondary and published.…”

Enhancing trust in clinical decision support systems: a framework for developers

“…It has, for example, been highlighted in the House of Lords Select Committee on Artificial Intelligence (AI) report 12 ; the Topol Review 13 ; a number of European Commission communications, [14][15][16] reports [17][18][19][20] and most recently a White Paper on AI 21 ; and investigated in the context of knowledge systems, for example, for Wikipedia. 22 Although it is an important concept, it is not always defined; rather, its meaning may be inferred. For example, the House of Lords Select Committee used the phrase 'public trust' eight times, 12 but the core concern appeared to be about confidence over the use of patient data, rather than patient perceptions regarding the efficacy (or otherwise) of the AI in question.…”