Adoption of agricultural technologies in the developing world: A meta-analysis dataset of the empirical literature.
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ABSTRACT:
SUBMITTER: Ruzzante S
PROVIDER: S-EPMC8479612 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:The potential benefit of mobile health (M-Health) in developing countries for improving the efficiency of health care service delivery, health care quality, and patient safety, as well as reducing cost, has been increasingly recognized and emphasized in the last few years. OBJECTIVE:Limited research has investigated the facilitators and barriers for the adoption of M-Health in developing countries to secure successful implementation of the technology. To fill this knowledge gap, we propose an integrative model that explains the patient's adoption behavior of M-Health in developing countries grounded on the unified theory of acceptance and use of technology, dual-factor model, and health belief model. METHOD:We empirically tested and evaluated the model based on data collected using a survey method from 280 patients living in a developing country. Partial least squares (PLS-SEM) technique was used for data analysis. RESULTS:The results showed that performance expectancy, effort expectancy, social influence, perceived health threat, M-Health app quality, and life quality expectancy have a direct positive effect on patients' intention to use M-Health. The results also showed that security and privacy risks have a direct negative effect on the patient's intention to use M-Health. However, resistance to change was found to have an indirect negative effect on patients' intention to use M-Health through the performance expectancy. CONCLUSION:The research contributes to the existing literature of health information systems and M-Health by better understanding how technological, social, and functional factors are associated with digital health applications and services use and success in the context of developing countries. With the widespread availability of mobile technologies and services and the growing demand for M-Health apps, this research can help guide the development of the next generation of M-Health apps with a focus on the needs of patients in developing countries. The research has several theoretical and practical implications for the health care industry, government, policy makers, and technology developers and designers.
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Project description:Adoption of new agricultural technologies is always at the center of policy interest in developing countries. In reality, despite the visible benefits of many of the new agricultural technologies, including machinery and management practices, farmers either do not adopt them or it takes a long time to begin the adoption process and scaling up. To enhance the provision of irrigation using surface water and to enhance irrigation efficiency, Bangladesh has been trying to introduce the axial-flow-pump (AFP) appropriate for surface water irrigation, which can lift up to 55% more water, conditional on the water head, than a conventional centrifugal pump. Despite the visible benefits of the AFP, the uptake of the AFP for irrigation is low in the targeted zone of Bangladesh. The present study demonstrates that the new technology must be modified to adapt to local demand and specifications. Most importantly, the price of the new technology must be competitive with the prices of the existing available substitute technologies to ensure a rapid uptake and scaling up of this new agricultural technology.
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Project description:The recent advancements in ICT have made it possible for teaching and learning to be conducted outside the four walls of a University. Furthermore, the recent COVID-19 pandemic that has crippled educational activities in all nations of the world has further revealed the urgent need for academic institutions to embrace and integrate alternative modes of teaching and learning via social media platforms and emerging technologies into existing teaching tools. This article contains data collected from 850 face to face University students during the COVID-19 pandemic lockdown. An online google form was used to elicit information from the students about their awareness and intention to use these alternative modes of teaching and learning. The questions were structured using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This data article includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file, the descriptive statistics, and reliability analysis computed for all the UTAUT variables. The dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies. The challenges that could inhibit the adoption of these technologies were also revealed.
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Project description:Low energy houses are forms of housing that use less energy from the design, technologies and building products from any source than a traditional or average contemporary house. The survey dataset examines architect׳s awareness and adoption of building envelope technologies (BET) for energy efficient housing in Lagos State, Nigeria. The dataset was based on seventy-four (74) returned questionnaires of both registered and non-registered Architects. A multistage sampling that involved cluster sampling and random sampling of architects in Lagos State was adopted. Descriptive statistical tools were used to present the dataset. The dataset contains the intent of promoting energy sustainability by architect while designing their building envelopes, the awareness of the building envelope strategies to adopt, factors influencing their adoption of these strategies, strategies that can be adopted to improve adoption of building envelope technologies for energy efficiency in housing units. The dataset can be used for evolving housing energy policy by decision makers.
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Project description:BackgroundMeta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature.ResultsWe present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability.ConclusionsAn empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies.
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Project description:ObjectiveTo estimate the coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) against concerned outcomes in real-world settings.MethodsStudies reporting COVID-19 VE from August 6, 2020 to October 6, 2021 were included. The summary VE (with 95% confidence intervals (95% CI)) against disease related to COVID-19 was estimated. The results were presented in forest plots. Predefined subgroup analyses and sensitivity analyses were also performed.ResultsA total of 51 records were included in this meta-analysis. In fully vaccinated populations, the VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19-related hospitalization, admission to the intensive care unit, and death was 89.1% (95% CI 85.6-92.6%), 97.2% (95% CI 96.1-98.3%), 97.4% (95% CI 96.0-98.8%), and 99.0% (95% CI 98.5-99.6%), respectively. The VE against infection in the general population aged ≥16 years, the elderly, and healthcare workers was 86.1% (95% CI 77.8-94.4%), 83.8% (95% CI 77.1-90.6%), and 95.3% (95% CI 92.0-98.6%), respectively. For those fully vaccinated against infection, the observed effectiveness of the Pfizer-BioNTech vaccine was 91.2% and of the Moderna vaccine was 98.1%, while the effectiveness of the CoronaVac vaccine was found to be 65.7%.ConclusionsThe COVID-19 vaccines are highly protective against SARS-CoV-2-related diseases in real-world settings.
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Project description:The present dataset ("dataset 3") is a subset of a large metastudy on AML classfication. It contains normalized gene expression values of 1181 samples. In total, three datasets were generated, each containing data of a different platforms: dataset 1 (Affymetrix HG-U133 A microarrays), dataset 2 (Affymetrix HG-U133 2.0 microarrays) and dataset 3 (RNA-seq). Dataset 3 was generated using the following strategy: All data sets published in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) on 20 September 2017 were reviewed for inclusion in the present study. Basic criteria for inclusion were the cell type under study (human peripheral blood mononuclear cells (PMBCs) and/or bone marrow samples) as well as the species (Homo sapiens). Furthermore, GEO SuperSeries were excluded to avoid duplicated samples. We filtered the datasets for data generated with high-throughput RNA sequencing (RNA-seq) and excluded studies with very small sample sizes (< 10 samples). We then applied a disease-specific search, in which we filtered for acute myeloid leukemia, other leukemia and healthy or non-leukemia-related samples. The results of this search strategy were then internally reviewed and data were excluded based on the following criteria: (i) exclusion of duplicated samples, (ii) exclusion of studies that sorted single cell types (e.g. T cells or B cells) prior to gene expression profiling, (iii) exclusion of studies with inaccessible data. Other than that, no studies were excluded from our analysis. In total, the datasets contained samples from the following GSE Series: GSE63085, GSE32874, GSE58335, GSE86884, GSE63703, GSE63646, GSE63816, GSE72790, GSE81259, GSE85712, GSE45735, GSE64655, GSE87186, GSE49642, GSE52656, GSE62190, GSE66917, GSE67039, GSE61162, GSE67184, GSE49601, GSE78785, GSE79970. All raw data files were downloaded from GEO. Transcript abundances were calculated using kallisto version 0.43.0 and all data was normalized with the R package DESeq2 (R version R-3.2.4, DESeq2 version 1.12.4) with standard parameters. Genome build hg38 was used for read alignment. No filtering of low-expressed genes was performed.
2019-12-18 | GSE122515 | GEO
Project description:BackgroundThe COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools.ObjectiveThe aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems.MethodsWe conducted a systematic review of early COVID-19-related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas.ResultsThe search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)-powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement.ConclusionsIn the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.
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Project description:BackgroundIt is universally accepted that primary healthcare is essential for achieving public health and that assessment of its performance is critical for continuous improvement. The World Health Organization's (WHO's) framework for performance assessment is a comprehensive global standard, but difficult to apply in developing countries because of financial and data constraints. This study aims to review the empirical literature on measures for Primary Health Centre (PHC) performance assessment in developing countries, and compare them for comprehensiveness with the aspects described by the WHO Framework.MethodsResearch articles published in English scientific journals between January 1979 and October 2016 were reviewed systematically. The reporting quality of the article and the quality of the measures were assessed with instruments adapted for the purpose of this study. Data was categorized and described.ResultsFifteen articles were included in the study out of 4359 articles reviewed. Nine articles used quantitative methods, one article used qualitative methods exclusively and five used mixed methods. Fourteen articles had a good description of the measurement properties. None of the articles presented validity tests of the measures but eleven articles presented measures that were well established. Mostly studies included components of personnel competencies (skilled/ non-skilled) and centre performance (patient satisfaction/cost /efficiency).ConclusionsIn comparison to the WHO framework, the measures in the articles were limited in scope as they did not represent all service components of PHCs. Hence, PHC performance assessment should include system components along with relevant measures of personnel performance beyond knowledge of protocols. Existing measures for PHC performance assessment in developing countries need to be validated and concise measures for neglected aspects need to be developed.
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Project description:While a variety of statistical models now exist for the spatio-temporal analysis of two-dimensional (surface) data collected over time, there are few published examples of analogous models for the spatial analysis of data taken over four dimensions: latitude, longitude, height or depth, and time. When taking account of the autocorrelation of data within and between dimensions, the notion of closeness often differs for each of the dimensions. Here, we consider a number of approaches to the analysis of such a dataset, which arises from an agricultural experiment exploring the impact of different cropping systems on soil moisture. The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors. In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm). Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping.
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