This paper is in the following e-collection/theme issue:

Crowdsourcing and Mechanical Turks (91)

Published on in Vol 21, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13792, first published .
Overcoming Barriers to Mobilizing Collective Intelligence in Research: Qualitative Study of Researchers With Experience of Collective Intelligence

Overcoming Barriers to Mobilizing Collective Intelligence in Research: Qualitative Study of Researchers With Experience of Collective Intelligence

Overcoming Barriers to Mobilizing Collective Intelligence in Research: Qualitative Study of Researchers With Experience of Collective Intelligence

Authors of this article:

Van Thu Nguyen1, 2, 3 Author Orcid Image ;   Bridget Young3 Author Orcid Image ;   Philippe Ravaud1, 2, 4 Author Orcid Image ;   Nivantha Naidoo1 Author Orcid Image ;   Mehdi Benchoufi1, 2, 4 Author Orcid Image ;   Isabelle Boutron1, 2, 4 Author Orcid Image
Article Authors Cited by (5) Tweetations (20) Metrics

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  2. Zhou L, Parmanto B. Development and Validation of a Comprehensive Well-Being Scale for People in the University Environment (Pitt Wellness Scale) Using a Crowdsourcing Approach: Cross-Sectional Study. Journal of Medical Internet Research 2020;22(4):e15075 View
  3. Yun J, Liu Z, Jeong E, Kim S, Kim K. The Difference in Open Innovation between Open Access and Closed Access, According to the Change of Collective Intelligence and Knowledge Amount. Sustainability 2022;14(5):2574 View
  4. Bedenik T, Cahir C, Bennett K. Building public trust and confidence in secondary use of health data for healthcare improvement and research: a qualitative study pre-protocol. HRB Open Research 2023;6:47 View
  5. DORNADIC A, LAVOIE N, DE FLORES S, TUYGAN E. Scaling Out (Not Only Up) Distributed Collaboration Models to Get Work Done. Ethnographic Praxis in Industry Conference Proceedings 2020;2020(1):149 View