A Crowdsourcing Decision Tree

Recently, I wrote a paper on crowdsourcing and public health with colleagues Kurt Ribisl, Tom Kirchner, and Jay Bernhardt. The paper has now been accepted for publication in the American Journal of Preventive Medicine and will appear in the February 2014 issue.

I have written papers like this before, but never for public health. I see it as my mission to advocate for the crowdsourcing model in various disciplines and professional contexts. I started with the urban planning field, then moved to public participation in governance more generally. And now I suppose it’s time to go to the public health arena.

With every paper I write on crowdsourcing, I refine my typology a bit. One of the biggest nuggets to come out of this paper was taking my typologyĀ  and laying it out in a decision tree, which practitioners can actually follow to figure out if and how crowdsourcing can help them. I’m grateful to Kurt, Tom, and Jay, who each helped make this decision tree clearer. Kurt kept pushing for more and better figures and tables in the paper, and I continually groaned (because they are really time-consuming to plan and create sometimes). But he’s right. It’s really much clearer now. Here is that decision tree:

Source: Brabham, D. C., Ribisl, K. M., Kirchner, T. R., & Bernhardt, J. M. (in press). Crowdsourcing applications for public health. American Journal of Preventive Medicine.

Source: Brabham, D. C., Ribisl, K. M., Kirchner, T. R., & Bernhardt, J. M. (in press). Crowdsourcing applications for public health. American Journal of Preventive Medicine.

It makes the typology clearer and more actionable, I think. What do you think?