Dataset Information

Creating Wikipedia articles on health and technology topics can empower the writers and benefit the community.

ABSTRACT:

Background

Publicly accessible information regarding imaging procedures is lacking, especially in non-English languages. Biomedical engineering students do not generally have opportunities to practice conveying scientific knowledge to the public.

Methods

As part of a Techniques and Clinical Usage of Medical Imaging Devices course, for extra credit, several biomedical engineering students choose to create and edit Wikipedia articles in the local language (Hebrew). The goal of this activity was to serve the local community, while improving students' abilities and self-perception in reading and reporting scientific knowledge. Following task completion, individual interviews were conducted with the students to assess the impact of the task on student personal development, sense of meaning and their view of their role in educating the public.

Results

Most students considered the task meaningful and impactful on society. Additional academic credit was not perceived as the most important incentive for participating.

Conclusions

Medical and other professional schools should seek to include tasks such as writing Wikipedia articles in their curricula. Educational assignments that integrate academic work, student identity development and direct community benefit can have a long-term beneficial impact on learners and society.

SUBMITTER: Moscovici K

PROVIDER: S-EPMC9109402 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Creating Wikipedia articles on health and technology topics can empower the writers and benefit the community.

Moscovici Khen K Arie Gal Ben GB Shelef Ilan I Kalet Adina A

BMC medical education 20220516 1

<h4>Background</h4>Publicly accessible information regarding imaging procedures is lacking, especially in non-English languages. Biomedical engineering students do not generally have opportunities to practice conveying scientific knowledge to the public.<h4>Methods</h4>As part of a Techniques and Clinical Usage of Medical Imaging Devices course, for extra credit, several biomedical engineering students choose to create and edit Wikipedia articles in the local language (Hebrew). The goal of this ...[more]

PMID: 35578333

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Project description:BACKGROUND:Consulting the Internet for health-related information is a common and widespread phenomenon, and Wikipedia is arguably one of the most important resources for health-related information. Therefore, it is relevant to identify factors that have an impact on the quality of health-related Wikipedia articles. OBJECTIVE:In our study we have hypothesized a positive effect of contributor experience on the quality of health-related Wikipedia articles. METHODS:We mined the edit history of all (as of February 2017) 18,805 articles that were listed in the categories on the portal health & fitness in the English language version of Wikipedia. We identified tags within the articles' edit histories, which indicated potential issues with regard to the respective article's quality or neutrality. Of all of the sampled articles, 99 (99/18,805, 0.53%) articles had at some point received at least one such tag. In our analysis we only considered those articles with a minimum of 10 edits (10,265 articles in total; 96 tagged articles, 0.94%). Additionally, to test our hypothesis, we constructed contributor profiles, where a profile consisted of all the articles edited by a contributor and the corresponding number of edits contributed. We did not differentiate between rollbacks and edits with novel content. RESULTS:Nonparametric Mann-Whitney U-tests indicated a higher number of previously edited articles for editors of the nontagged articles (mean rank tagged 2348.23, mean rank nontagged 5159.29; U=9.25, P<.001). However, we did not find a significant difference for the contributors' total number of edits (mean rank tagged 4872.85, mean rank nontagged 5135.48; U=0.87, P=.39). Using logistic regression analysis with the respective article's number of edits and number of editors as covariates, only the number of edited articles yielded a significant effect on the article's status as tagged versus nontagged (dummy-coded; Nagelkerke R2 for the full model=.17; B [SE B]=-0.001 [0.00]; Wald c2 [1]=19.70; P<.001), whereas we again found no significant effect for the mere number of edits (Nagelkerke R2 for the full model=.15; B [SE B]=0.000 [0.01]; Wald c2 [1]=0.01; P=.94). CONCLUSIONS:Our findings indicate an effect of contributor experience on the quality of health-related Wikipedia articles. However, only the number of previously edited articles was a predictor of the articles' quality but not the mere volume of edits. More research is needed to disentangle the different aspects of contributor experience. We have discussed the implications of our findings with respect to ensuring the quality of health-related information in collaborative knowledge-building platforms.

Project description:BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Dataset Information

Creating Wikipedia articles on health and technology topics can empower the writers and benefit the community.

Background

Methods

Results

Conclusions

Publications

Creating Wikipedia articles on health and technology topics can empower the writers and benefit the community.

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