Published on in Vol 20, No 5 (2018): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9330, first published .
Mapping of Crowdsourcing in Health: Systematic Review

Mapping of Crowdsourcing in Health: Systematic Review

Mapping of Crowdsourcing in Health: Systematic Review

Journals

  1. Borda A, Gray K, Fu Y. Research data management in health and biomedical citizen science: practices and prospects. JAMIA Open 2020;3(1):113 View
  2. Pratap A, Allred R, Duffy J, Rivera D, Lee H, Renn B, Areán P. Contemporary Views of Research Participant Willingness to Participate and Share Digital Data in Biomedical Research. JAMA Network Open 2019;2(11):e1915717 View
  3. Tuerk P, Schaeffer C, McGuire J, Adams Larsen M, Capobianco N, Piacentini J. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions. Current Psychiatry Reports 2019;21(10) View
  4. Pluye P, Granikov V, Tang D, Granikov V, Pluye P. Facilitators and barriers associated with the implementation of an innovative cross-disciplinary monitoring of the scientific literature: The Collaborative eBibliography on Mixed Methods (CeBoMM) – A research protocol. Education for Information 2020;36(1):81 View
  5. Petrović N, Moyà-Alcover G, Varona J, Jaume-i-Capó A. Crowdsourcing human-based computation for medical image analysis: A systematic literature review. Health Informatics Journal 2020;26(4):2446 View
  6. St John-Matthews J, Newton P, Grant A, Robinson L. Crowdsourcing in health professions education: What radiography educators can learn from other disciplines. Radiography 2019;25(2):164 View
  7. Bassi H, Lee C, Misener L, Johnson A. Exploring the characteristics of crowdsourcing: An online observational study. Journal of Information Science 2020;46(3):291 View
  8. van Niekerk L, Ongkeko A, Hounsell R, Msiska B, Mathanga D, Mothe J, Juban N, Awor P, Balabanova D. Crowdsourcing to identify social innovation initiatives in health in low- and middle-income countries. Infectious Diseases of Poverty 2020;9(1) View
  9. Wu D, Ong J, Tang W, Ritchwood T, Walker J, Iwelunmor J, Tucker J. Crowdsourcing Methods to Enhance HIV and Sexual Health Services: A Scoping Review and Qualitative Synthesis. JAIDS Journal of Acquired Immune Deficiency Syndromes 2019;82(3):S271 View
  10. Latkin C, Dayton L, Yi G, Konstantopoulos A, Boodram B. Trust in a COVID-19 vaccine in the U.S.: A social-ecological perspective. Social Science & Medicine 2021;270:113684 View
  11. Vermicelli S, Cricelli L, Grimaldi M. How can crowdsourcing help tackle the COVID‐19 pandemic? An explorative overview of innovative collaborative practices. R&D Management 2021;51(2):183 View
  12. Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R. Deep learning-enabled medical computer vision. npj Digital Medicine 2021;4(1) View
  13. Nguyen V, Benchoufi M, Young B, Ghosn L, Ravaud P, Boutron I. A scoping review provided a framework for new ways of doing research through mobilizing collective intelligence. Journal of Clinical Epidemiology 2019;110:1 View
  14. Renn B, Hoeft T, Lee H, Bauer A, Areán P. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. npj Digital Medicine 2019;2(1) View
  15. Ren C, Tucker J, Tang W, Tao X, Liao M, Wang G, Jiao K, Xu Z, Zhao Z, Yan Y, Lin Y, Li C, Wang L, Li Y, Kang D, Ma W. Digital crowdsourced intervention to promote HIV testing among MSM in China: study protocol for a cluster randomized controlled trial. Trials 2020;21(1) View
  16. Kennedy-Metz L, Mascagni P, Torralba A, Dias R, Perona P, Shah J, Padoy N, Zenati M. Computer Vision in the Operating Room: Opportunities and Caveats. IEEE Transactions on Medical Robotics and Bionics 2021;3(1):2 View
  17. Desai A, Warner J, Kuderer N, Thompson M, Painter C, Lyman G, Lopes G. Crowdsourcing a crisis response for COVID-19 in oncology. Nature Cancer 2020;1(5):473 View
  18. Wong S, Smith S, Jacova C. Older Adults With Subjective Cognitive Decline Worry About the Emotional Impact of Cognitive Test Results. Alzheimer Disease & Associated Disorders 2020;34(2):135 View
  19. Szmelter-Jarosz A, Rześny-Cieplińska J. Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective. Sustainability 2019;12(1):317 View
  20. Créquit P, Boutron I, Meerpohl J, Williams H, Craig J, Ravaud P. Future of evidence ecosystem series: 2. current opportunities and need for better tools and methods. Journal of Clinical Epidemiology 2020;123:143 View
  21. Sood E, Wysocki T, Alderfer M, Aroian K, Christofferson J, Karpyn A, Kazak A, Pierce J. Topical Review: Crowdsourcing as a Novel Approach to Qualitative Research. Journal of Pediatric Psychology 2021;46(2):189 View
  22. Wang , Xia , Li , Wang . A Bibliometric Analysis of Crowdsourcing in the Field of Public Health. International Journal of Environmental Research and Public Health 2019;16(20):3825 View
  23. Beto J, Metallinos-Katsaras E, Leung C. Crowdsourcing: A Critical Reflection on This New Frontier of Participant Recruiting in Nutrition and Dietetics Research. Journal of the Academy of Nutrition and Dietetics 2020;120(2):193 View
  24. Krusche M, Burmester G, Knitza J. Digital crowdsourcing: unleashing its power in rheumatology. Annals of the Rheumatic Diseases 2020;79(9):1139 View
  25. Porsdam Mann S, Savulescu J, Ravaud P, Benchoufi M. Blockchain, consent and prosent for medical research. Journal of Medical Ethics 2021;47(4):244 View
  26. Gardašević G, Katzis K, Bajić D, Berbakov L. Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare. Sensors 2020;20(13):3619 View
  27. Geng Y, Huang P, Huang Y. Crowdsourcing in Nursing Education: A Possibility of Creating a Personalized Online Learning Environment for Student Nurses in the Post-COVID Era. Sustainability 2021;13(6):3413 View
  28. Latkin C, Dayton L, Miller J, Yi G, Jaleel A, Nwosu C, Yang C, Falade-Nwulia O. Behavioral and Attitudinal Correlates of Trusted Sources of COVID-19 Vaccine Information in the US. Behavioral Sciences 2021;11(4):56 View
  29. Noel-Storr A, Redmond P, Lamé G, Liberati E, Kelly S, Miller L, Dooley G, Paterson A, Burt J. Crowdsourcing citation-screening in a mixed-studies systematic review: a feasibility study. BMC Medical Research Methodology 2021;21(1) View

Books/Policy Documents

  1. Song M, Tabi K, Krausz M. Innovations in Global Mental Health. View
  2. Hall D, Hibbert A, Vesala M, Kerr M, Harrison S. Tinnitus - An Interdisciplinary Approach Towards Individualized Treatment: From Heterogeneity to Personalized Medicine. View
  3. Sathya D. , Sudha V. , Jagadeesan D. . Handbook of Research on Applications and Implementations of Machine Learning Techniques. View