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Harvesting Collective Agreement in Community Oriented Surveys: The Medical Case

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Abstract

The chapter discusses the role of simple and lightweight Web-based systems in promoting a different approach to the externalization of practice-related knowledge within communities of professionals. This approach exploits common online questionnaire systems to collect the preferences of large numbers of domain experts to interesting paradigmatic work cases and proposes a statistically sound evaluation of these responses to evaluate the agreement reached within the community. We tested this approach in a case study that involved a large international medical association, that we chose as an example of a large and highly distributed community of expert professionals; in this study we challenged more than 1,000 surgeons about some border-line clinical cases where tacit notions based on life-long practice and situated experiences coexist (and sometimes clash) with scientific evidences drawn from the specialistic literature. We make the point that a sound evaluation of the collective agreement is a necessary precondition to use such lean Web-based tools in bottom-up knowledge elicitation initiatives. To this aim, existing measures of agreement and survey-related heuristics can be exploited to get a more precise picture of the “opinion of the many” in collective settings like communities of practice.

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Notes

  1. 1.

    http://www.mmslists.com/.

  2. 2.

    http://www.nhs.net.

  3. 3.

    Obviously a doctor does not choose a treatment by chance; but it is the doctors that are (should be) recruited by chance.

  4. 4.

    Not to be confused with the Cohen Kappa, suitable for multi-case two-rater assessments.

  5. 5.

    In this latter case we compared the values of Chi with the Kendall’s coefficient of concordance, which is a normalized score between 0 and 1 as the above mentioned Alpha and Kappa.

  6. 6.

    These are, respectively, evidences of level II-3 and III or level C and D according to the evidence ranking developed by either the U.S. Preventive Services Task Force or the Oxford Centre for Evidence-based Medicine.

  7. 7.

    The logistic regression model we obtained is represented by the function y(x) = a/b + ce-ax with \(a \simeq 1.04,\,b \simeq 0.004,\,c \simeq 0.06\) and it is indicated with P(t) in Fig. 6.2.

  8. 8.

    This holds in the assumption that responses from the second turn are representative of the non-responses, and that people that can get convinced by a single reminder end up by exhibiting similar opinions to those that, conversely, are refractory to any reminder at all.

  9. 9.

    y′ = abce cx/(b + e cx)2.

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Cabitza, F. (2012). Harvesting Collective Agreement in Community Oriented Surveys: The Medical Case. In: Dugdale, J., Masclet, C., Grasso, M., Boujut, JF., Hassanaly, P. (eds) From Research to Practice in the Design of Cooperative Systems: Results and Open Challenges. Springer, London. https://doi.org/10.1007/978-1-4471-4093-1_6

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  • DOI: https://doi.org/10.1007/978-1-4471-4093-1_6

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