This version of the glossary is substantially changed from the original. Some terms have been omitted, many have been modified in light of practical experiences and the evolution in concepts, and new terms have been added. The list of terms is not intended to be either exhaustive or exclusive, and d...raws upon the wide range of disciplines in which health promotion has its roots. Wherever possible, definitions are sourced or derived from existing, publicly accessible WHO documents. Specific sources are referenced, and where possible a web link is also provided to facilitate access to source documents. Hyperlinks were correct at the time of publication but are subject 2 Health Promotion Glossary of Terms 2021 to inevitable change. In some examples the definitions have been adapted to reflect the application of a term to the current health promotion context. Where relevant, this focus is acknowledged in individual definitions.
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Case Studies on Building Resilience in the Horn of Africa
This annual report highlights the work of the WHO from January to June 2021 ( December 2021). The activities featured herein are by no means exhausted but implemented with technical and financial support through WHO in Nigeria; facilitated by its presence at all levels of governance (national, state..., local government, and wards).
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Action on behalf of unaccompanied and separated children should be guided by principles enshrined in international standards. The validity of these principles has been confirmed by experience and lessons learnt from conflicts and natural disasters in recent years. The objective of the present public...ation is to outline the guiding principles which form the basis for action in this regard.
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This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. In addition to visual inspection of laboratory results, we examined how accounting for potential misclassification of HIV status through Bayesian latent ...class models affected the prevalence estimates. Two types of Bayesian models were specified: a model that only uses the individual dichotomous test results and a continuous model that uses the quantitative information of the EIA (i.e., the signal-to-cutoff values). Overall, we found that adjusted prevalence estimates matched the surveys’ original results, with overlapping uncertainty intervals. This suggested that misclassification of HIV status should not affect the prevalence estimates in most surveys. However, our analyses suggested that two surveys may be problematic. The prevalence could have been overestimated in the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, although the magnitude of overestimation remains difficult to ascertain. Interpreting results from the Uganda survey is difficult because of the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite the limitations of our latent class models, our analyses suggest that prevalence estimates from most of the surveys reviewed are not affected by sample misclassification.
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India AIDS Response Report 2014