6 research outputs found

    Selected Psychographic Characteristics and their Relationship with Sexual-Risk Outcomes.

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    <p>These partial regression plots <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099987#pone.0099987-Brehney1" target="_blank">[34]</a> show the predicted influence of significant behavioral characteristics (x-axes) on sexual risk-taking outcomes (y-axes), controlling for demographic variables. Sexual risk-taking outcomes, by panel, include (a) number of lifetime sexual partners, (b) probability of previous HIV testing, and (c) probability of non-virgin status.</p

    Hierarchical Regression Analyses.

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    <p><i>Note</i>. Sets of predictors were added in sequence from M0 to M7. M0  =  Null Model (intercept only), M1 =  demographic variables (age, gender, highest education achieved, employment status, Maasai vs. other ethnicity), M2 =  village membership, M3 =  weekly media consumption (radio, television, print media), M4 =  HIV knowledge, M5 =  psychographic factors. Change in model fit was assessed via likelihood ratio testing: i.e., change in deviance relative to change in degrees of freedom. Sets shown to improve model fit (<i>* p</i><.05) were carried forward in subsequent analyses.</p>a<p>Deviance (-2*log-likelihood) is reported for each model. R<sup>2</sup> is reported in brackets for best-fitting logistic models.</p>b<p>Analyses were carried out on data from the subset of individuals who reported previous sexual activity.</p>c<p>Age was not included in the set of basic demographic variables in this analysis.</p

    Predictors of Sexual-Risk Taking.

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    <p><i>Note</i>. Raw regression weights are reported with standard errors (in parentheses). Dashes (−) reflect sets of items that were not included in an analysis due to their negligible contribution to improvement in model fit. * <i>p</i><.05</p>a<p>Regression weights represent change in log odds (e.g.,.77 gives <i>e<sup>0</sup></i><sup>.77</sup> = 2.16× increase in odds of engaging in sexual behavior for females relative to males, given other covariates in the model.</p>b<p>Analyses carried out on data from the subset of individuals who reported previous sexual activity.</p>c<p>Exponentiated coefficients show the multiplicative increase in expected number of lifetime sex partners (e.g.,.34 gives <i>e<sup>0</sup></i><sup>.34</sup> = 1.4× increase in number of sexual partners for youth with primary education relative to those without).</p>d<p>Coefficients represent change in log odds of incremental probability of virginity loss. As examples, holding other variables constant, [A] completion of secondary education reduces the incremental (yearly by age) hazard of virginity loss by a factor of <i>e</i><sup>−0.73</sup> = 0.48 or 52% (1–.48) relative to those who have not completed primary school, and [B] Maasai have an increased yearly hazard of virginity loss equal to <i>e</i><sup>0.35</sup> = 1.42 or 42% relative to non-Maasai.</p>e<p>Age was not included in the set of basic demographic predictor variables for this analysis.</p>f<p>Number of villages (out of 7, excluding reference village) showing a significant positive relationship to outcome.</p>g<p>Media consumption is included for consistency, despite having been excluded as a predictor set in each of the best-fitting models in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099987#pone-0099987-t002" target="_blank">Table 2</a>.</p

    Map Showing the Villages in which the Youth Survey was Conducted.

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    <p>Red filled circles indicate primary study villages (#s 7–14), and blue filled circles indicate pilot study villages (#s 1–6).</p

    Participant Demographics.

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    a<p>Almost invariably this is in addition to farming.</p>b<p>Non-responders were included in calculation of percentage as 'untested'.</p>c<p>Only those who reported being sexually active were included in the calculation.</p
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