Moments in the Production of Space: Developing a Generic Adolescent
Girls and Young Women Health Information Systems in Zimbabwe
- URL: http://arxiv.org/abs/2108.09811v1
- Date: Sun, 22 Aug 2021 18:22:17 GMT
- Title: Moments in the Production of Space: Developing a Generic Adolescent
Girls and Young Women Health Information Systems in Zimbabwe
- Authors: Rangarirai Matavire, J{\o}rn Braa, Shorai Huwa, Lameck Munangaidzwa,
Zeferino Saugene, Isaac Taramusi and Bob Jolliffe
- Abstract summary: This study follows a project to develop a generic health information systems solution.
It provides a means to monitor and evaluate the successes of the AGYW initiative in reducing new infections.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: With global targets to end AIDS by 2030 and to eliminate new HIV infections,
Adolescent Girls and Young Women (AGYW) are seen to be particularly vulnerable,
especially in Sub Saharan Africa. Numerous nations have therefore rolled out
interventions to provide services to remove the determinants of vulnerability,
such as limited education, early marriage, poverty, domestic violence, and
exposure by male partners. Within this context, subpopulations such as sex
workers increase the vulnerability amongst AGYW and are also supported through
prevention programming. This study follows a project to develop a generic
health information systems solution to provide a means to monitor and evaluate
the successes of the AGYW initiative in reducing new infections. It borrows
theoretical ideas from Henri Lefebvre's theory of moments to describe the
process in which the space for the development of the solution is produced.
Related papers
- Infectious Disease Forecasting in India using LLM's and Deep Learning [0.3141085922386211]
This paper implements deep learning algorithms and LLM's to predict the severity of infectious disease outbreaks.
The insights from our research aim to assist in creating a robust predictive system for any outbreaks in the future.
arXiv Detail & Related papers (2024-10-26T12:54:09Z) - SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness [73.73883111570458]
We introduce the first multilingual Event Extraction framework for extracting epidemic event information for a wide range of diseases and languages.
Annotating data in every language is infeasible; thus we develop zero-shot cross-lingual cross-disease models.
Our framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 from Chinese Weibo posts without any training in Chinese.
arXiv Detail & Related papers (2024-10-24T03:03:54Z) - Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
generative AI, particularly large language models (LLMs), become increasingly integrated into production applications.
New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks.
This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
arXiv Detail & Related papers (2024-09-23T10:18:10Z) - SUKHSANDESH: An Avatar Therapeutic Question Answering Platform for Sexual Education in Rural India [16.8154824364057]
In countries like India, where adolescents form the largest demographic group, they face significant vulnerabilities concerning sexual health.
Our proposal aims to provide a safe and trustworthy platform for sexual education to the vulnerable rural Indian population.
By utilizing information retrieval techniques and large language models, SUKHSANDESH will deliver effective responses to user queries.
arXiv Detail & Related papers (2024-05-03T05:19:09Z) - Event Detection from Social Media for Epidemic Prediction [76.90779562626541]
We develop a framework to extract and analyze epidemic-related events from social media posts.
Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics.
We show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
arXiv Detail & Related papers (2024-04-02T06:31:17Z) - Sistemas de informaci\'on de salud en contextos extremos: Uso de
tel\'efonos m\'oviles para combatir el sida en Uganda [0.0]
This paper studies an m-health system for HIV patients in the Kalangala region of Uganda.
It shows that the rich interaction between social context and technology should be considered a central concern when designing or deploying such systems.
arXiv Detail & Related papers (2024-03-10T03:44:16Z) - Time Series Forecasting of HIV/AIDS in the Philippines Using Deep
Learning: Does COVID-19 Epidemic Matter? [0.0]
The HIV/AIDS epidemic in the Philippines is the one that is spreading the quickest in the western Pacific.
Although the full effects of COVID-19 on HIV services and development are still unknown, it is predicted that such disruptions could lead to an increase in HIV casualties.
This study uses Multilayer Perceptron Neural Network to forecast time series during the period when the COVID-19 pandemic strikes the nation.
arXiv Detail & Related papers (2024-01-11T14:11:30Z) - A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S.
Ending the HIV Epidemic Plan [2.498439320062193]
Ending the HIV Epidemic initiative aims to reduce new infections by 90% by 2030.
Existing HIV decision analytic models either evaluate specific cities or the overall national population, thus overlooking jurisdictional interactions or differences.
We propose a multi-agent reinforcement learning (MARL) model, that enables jurisdiction-specific decision analyses but in an environment with cross-jurisdictional epidemiological interactions.
arXiv Detail & Related papers (2023-11-01T21:19:35Z) - The Role of Robotics in Infectious Disease Crises [46.43737882437637]
The recent coronavirus pandemic has highlighted the challenges faced by the healthcare, public safety, and economic systems when confronted with a surge in patients.
There is a complementary need to anticipate and address the engineering challenges associated with infectious disease emergencies.
As technical capabilities advance and as the installed base of robotic systems increases in the future, they could play a much more significant role in future crises.
arXiv Detail & Related papers (2020-10-19T22:54:12Z) - Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918.
Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases.
We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
arXiv Detail & Related papers (2020-04-16T15:53:42Z) - Mapping the Landscape of Artificial Intelligence Applications against
COVID-19 [59.30734371401316]
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization.
We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
arXiv Detail & Related papers (2020-03-25T12:30:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.