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Breast self-examination: Knowledge, practice and associated factors among 20 to 49 years aged women in Butwal sub-metropolitan, Rupandehi, Nepal
Breast cancer is the second most common cancer in the world and also among Nepalese women. Breast self-examination is an important, cheap, and easy method for early diagnosis of breast cancer which can be cured in the majority of cases if diagnosed in the early stages. In developing countries like Nepal where the awareness regarding breast cancer and breast self-examination is poor, breast cancers are diagnosed at late stages resulting in a poor prognosis of the disease. The study assessed knowledge, practice, and factors associated with breast self-examination.
A cross-sectional survey was carried out among 262 women in the Butwal sub-metropolitan adopting multi-stage sampling. A pre-tested structured interview schedule and an observation checklist were used to collect the data. Data was entered in EPI-data and necessary univariate, bivariate, and multivariate analyses were done in SPSS.
The study found that more than half of the participants (55.3%) had poor knowledge of BSE. Only one-fourth (27.1%) of them were practicing BSE and among them, most of them (93.0%) had poor practice. The factors such as ethnicity from Brahmin/Chhetri [AOR = 2.099, 95% CI (1.106-3.981)], use of contraceptive devices [AOR = 9.487, 95% CI (2.166-41.558)], personal history of breast lump [AOR = 12.502, 95% CI (1.639-95.387)], family history of breast cancer [AOR = 5.729, 95% CI (1.337-97.512)], and knowledge of BSE [AOR = 4.407, 95% CI = 2.160-34.650)] were significant determinants of BSE practice among 20-49 years women.
The study concluded that most of the women had poor knowledge and practice of breast self-examination. The study also indicated the influence of ethnicity, contraceptives, personal and family history of cancer/early warning signs, and knowledge for practicing breast self-examination. There is an immediate need to increase the knowledge and practice of breast self-examination to prevent and detect breast cancer in its early stage.
Journal Article
Implementation science in resource-poor countries and communities
Background
Implementation science in resource-poor countries and communities is arguably more important than implementation science in resource-rich settings, because resource poverty requires novel solutions to ensure that research results are translated into routine practice and benefit the largest possible number of people.
Methods
We reviewed the role of resources in the extant implementation science frameworks and literature. We analyzed opportunities for implementation science in resource-poor countries and communities, as well as threats to the realization of these opportunities.
Results
Many of the frameworks that provide theoretical guidance for implementation science view resources as contextual factors that are important to (i) predict the feasibility of implementation of research results in routine practice, (ii) explain implementation success and failure, (iii) adapt novel evidence-based practices to local constraints, and (iv) design the implementation process to account for local constraints. Implementation science for resource-poor settings shifts this view from “resources as context” to “resources as primary research object.” We find a growing body of implementation research aiming to discover and test novel approaches to generate resources for the delivery of evidence-based practice in routine care, including approaches to create higher-skilled health workers—through tele-education and telemedicine, freeing up higher-skilled health workers—through task-shifting and new technologies and models of care, and increasing laboratory capacity through new technologies and the availability of medicines through supply chain innovations. In contrast, only few studies have investigated approaches to change the behavior and utilization of healthcare resources in resource-poor settings. We identify three specific opportunities for implementation science in resource-poor settings. First, intervention and methods innovations thrive under constraints. Second, reverse innovation transferring novel approaches from resource-poor to research-rich settings will gain in importance. Third, policy makers in resource-poor countries tend to be open for close collaboration with scientists in implementation research projects aimed at informing national and local policy.
Conclusions
Implementation science in resource-poor countries and communities offers important opportunities for future discoveries and reverse innovation. To harness this potential, funders need to strongly support research projects in resource-poor settings, as well as the training of the next generation of implementation scientists working on new ways to create healthcare resources where they lack most and to ensure that those resources are utilized to deliver care that is based on the latest research results.
Journal Article
Decriminalizing Indoor Prostitution
Most governments in the world, including the U.S., prohibit sex work. Given these types of laws rarely change and are fairly uniform across regions, our knowledge about the impact of decriminalizing sex work is largely conjectural. We exploit the fact that a Rhode Island District Court judge unexpectedly decriminalized indoor sex work to provide causal estimates of the impact of decriminalization on the composition of the sex market, reported rape offences, and sexually transmitted infections. While decriminalization increases the size of the indoor sex market, reported rape offences fall by 30% and female gonorrhoea incidence declines by over 40%.
Journal Article
Beginning AI Bot frameworks : getting started with bot development
Want to build your first AI bot but don't know where to start? This book provides a comprehensive look at all the major bot frameworks available. You'll learn the basics for each framework in one place and get a clear picture for which one is best for your needs. \"Beginning AI Bot Frameworks\" starts with an overview of bot development and then looks at Google Wit.ai and APi.ai functions, IBM Watson, AWS bots with Lambda, FlockOS and TensorFlow. Additionally, it touches on Deep Learning and how bot frameworks can be extended to mixed reality with Hololens. By the end, you'll have mastered the different bot frameworks available and finally have the confidence to develop intelligent AI Chatbots of their own.
Book
The rhetoric of Hindu India : language and urban nationalism
\"Examines the rise of the urban right-wing Hindu nationalist ideology in India called Hindutva between 1984 and 2004\"-- Provided by publisher.
BOOK
Effective network intrusion detection by addressing class imbalance with deep neural networks multimedia tools and applications
The Intrusion Detection System plays a significant role in discovering malicious activities and provides better network security solutions than other conventional defense techniques such as firewalls. With the aid of machine learning-based techniques, such systems can detect attacks more accurately by identifying the relevant data patterns. However, the nature of network data, time-varying environment, and unknown occurrence of attacks made the learning task very complex. We propose a deep neural network that utilizes the classifier-level class imbalance solution to solve this problem effectively. Initially, the network data is preprocessed through data conversion followed by the min-max normalization method. Then, normalized data is fed to neural network where the cross-entropy function is modified to address the class imbalance problem. It is achieved by weighting the classes while training the classifier. The extensive experiments are performed on two challenging datasets, namely NSL-KDD and UNSW-NB15, to establish the superiority of the proposed approach. It includes comparisons with commonly employed imbalance approaches such as under-sampling, over-sampling, and bagging as well as existing works. The proposed approach attains 85.56% and 90.76% classification accuracy on NSL-KDD and UNSW-NB15 datasets, respectively. These outcomes outperformed data-level imbalance methods and existing works that validate the need to incorporate class imbalance for network traffic categorization.
Journal Article