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. 2016 Aug 1;18(8):e204. doi: 10.2196/jmir.5461

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©Tiago Taveira-Gomes, Patrícia Ferreira, Isabel Taveira-Gomes, Milton Severo, Maria Amélia Ferreira. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 01.08.2016.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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Prevalence of articles per intervention feature and intervention endpoint latent class. Horizontal axis ranges between 0 and 100 on a squared root scale. Point color specifies the probability of assigning a paper to each class based on the presence of each variable (gray indicates a probability of 0, ranging to dark blue indicating the highest probability). Only variables regarding assessment of knowledge, attitudes, skills, and online activity (the 4 last panels) were used to determine intervention end point latent classes. CBL: computer-based learning.