یادگیری در جوامع پشتیبانی سلامت آنلاین
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یادگیری در جوامع پشتیبانی سلامت آنلاین

عنوان فارسی مقاله: مادیت بخشیدن به مشاوره، یادگیری و هوش جمعی برجسته در جوامع پشتیبانی سلامت آنلاین
عنوان انگلیسی مقاله: Advice reification, learning, and emergent collective intelligence in online health support communities
مجله/کنفرانس: نقش کامپیوتر در رفتار انسان – Computers in Human Behavior
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات، مهندسی صنایع
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده، مهندسی سیستم های سلامت
کلمات کلیدی فارسی: جامعه سلامت آنلاین، مشاوره سلامتی، جامعه عمل، مادیت بخشیدن
کلمات کلیدی انگلیسی: Online health community، Health advice، Community of practice، Reification
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.chb.2019.05.028
دانشگاه: Department of Media and Information, Michigan State University, East Lansing, MI, 48824, USA
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 5.876 در سال 2018
شاخص H_index: 137 در سال 2019
شاخص SJR: 1.711 در سال 2018
شناسه ISSN: 0747-5632
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 14
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13647
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1. Introduction


2. Material and methods


3. Results


4. Discussion & future work


5. Conclusion


Appendix A.


References

نمونه متن انگلیسی مقاله

Abstract


Online health support forums utilize straightforward online discussion designs to create a sociotechnical space where people can seek social support from others. The advice generated in these forums exists as an archival resource for future health information seekers. The present study uses mixed methods to investigate how invisible social processes lead advice to be adapted over time by forum members. Drawing on the construct of ‘reification’ from the communities of practice (COP) literature, we operationalize the reification of advice (RoA) as a process by which advice is developed across multiple discussion threads, and construct an algorithmic procedure to extract posts that trace this process. We evaluate our algorithm with crowd-workers, and perform an inductive, qualitative analysis to identify different modes of advice reification. We suggest that RoA could be used as the basis of a mid-level theory that treats online support communities and bundles of advice trajectories embedded in a shifting sociotechnical context. In our closing analysis, we propose that our approach might be a first step in an algorithmic procedure for assessing advice quality, drawing on the idea that reified advice may be considered a product of the collective intelligence of an online health support community.


Introduction


People who participate in online social platforms leave digital traces of their activities, which can in turn become a resource for others (e.g. articles on Wikipedia, code on GitHub, answers on StackOverflow). Social computing platforms employ sociotechnical infrastructures designed to scaffold longitudinal process that adapt and, hopefully, improve the quality of these resources (Bryant, Forte, & Bruckman, 2005; Crowston, Wei, Li, & Howison, 2006; Parnin, Treude, & Grammel, 2012; Solomon & Wash, 2014). Highlighting their ability to harness the collective efforts of online contributors, such social computing platforms have been described as a kind of designed collective intelligence (Kittur & Kraut, 2008; Malone, Laubacher, & Dellarocas, 2009; Quinn & Bederson, 2011). In other cases, resources generated by online platforms are a side effect of online engagement. Such is often the case with online health support communities (OHC). OHCs are places where people can go to find social support for health conditions. In many cases OHCs are organized as Question & Answer style discussions, and built upon wellestablished threaded discussion forum technologies (Davison, Pennebaker, & Dickerson, 2000; Maloney-Krichmar & Preece, 2005). The first post in each threaded conversation is usually a question (a request for some kind of support) and the remainder of the conversation is devoted in part to responding to the initial question while also providing long-time forum members with an opportunity for richer interactions (Introne, Semaan, & Goggins, 2016). These conversations often yield advice for the original asker, and this advice may also be considered a resource for future seekers.

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