which type of research frequently involves the inductive method?
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The assessment was implemented separately in the Mainland of Tanzania and the semi-autonomous state of Zanzibar. The East African Community EACa regional intergovernmental organization of six partner states, has worked for the past decade to improve the efficacy and efficiency of health services in member countries, with a focus on strengthening digital health also known as eHealth. The EAC has committed to supporting regional actions to strengthen the enabling environment for effective digital health information systems HIS.
This brief highlights the results of this assessment for the semi-autonomous state of Zanzibar.
UBC Theses and Dissertations
This brief highlights the results of this assessment for Mainland Tanzania. Stunting and wasting are still global issues, with an estimated million children under five with stunted growth and 49 million children under five suffering from wasting worldwide. Both stunting https://digitales.com.au/blog/wp-content/custom/general-motors-and-the-affecting-factors-of/existential-question-definition.php wasting share underlying risk factors that derive from several different levels of influence.
Existing studies focus on demographic and health indicators, such as those that are available in the Demographic and Health Surveys DHS. However, additional influences on these outcomes are also agricultural and community-level indicators that are not included in conventional demographic and health surveys. Studies are ths to trial the linking of these data and to provide lessons learned for others seeking to do the same.
The increased availability of data from multiple sources in low- and middle-income countries in recent years, combined with advances in data science, have stimulated an increased interest in using existing data in innovative ways to bring new insights to population, health, and nutrition problems. MEASURE Evaluation was contracted to do just that—to conduct an analysis of publicly available secondary data using innovative linking methods to better understand a broader range of drivers of wasting and stunting, particularly in contexts with stagnant or increasing wasting levels and decreasing stunting trends.
This study also sought to use machine learning to identify additional or unique patterns of indicators that influence stunting and wasting.]
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