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Supporting Contraceptive Decision-Making in the Intermediated Pharmacy Setting in Kenya
Authors:
Lisa Orii,
Elizabeth K Harrington,
Serah Gitome,
Nelson Kiprotich Cheruiyot,
Elizabeth Anne Bukusi,
Sandy Cheng,
Ariel Fu,
Khushi Khandelwal,
Shrimayee Narasimhan,
Richard Anderson
Abstract:
Adolescent girls and young women (AGYW) in sub-Saharan Africa face unique barriers to contraceptive access and lack AGYW-centered contraceptive decision-support resources. To empower AGYW to make informed choices and improve reproductive health outcomes, we developed a tablet-based application to provide contraceptive education and decision-making support in the pharmacy setting - a key source of…
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Adolescent girls and young women (AGYW) in sub-Saharan Africa face unique barriers to contraceptive access and lack AGYW-centered contraceptive decision-support resources. To empower AGYW to make informed choices and improve reproductive health outcomes, we developed a tablet-based application to provide contraceptive education and decision-making support in the pharmacy setting - a key source of contraceptive services for AGYW - in Kenya. We conducted workshops with AGYW and pharmacy providers in Kenya to gather app feedback and understand how to integrate the intervention into the pharmacy setting. Our analysis highlights how intermediated interactions - a multiuser, cooperative effort to enable technology use and information access - could inform a successful contraceptive intervention in Kenya. The potential strengths of intermediation in our setting inform implications for technological health interventions in intermediated scenarios in \lrem{LMICs}\ladd{low- and middle-income countries}, including challenges and opportunities for extending impact to different populations and integrating technology into resource-constrained healthcare settings.
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Submitted 7 February, 2025;
originally announced February 2025.
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HIV Client Perspectives on Digital Health in Malawi
Authors:
Lisa Orii,
Caryl Feldacker,
Jacqueline Madalitso Huwa,
Agness Thawani,
Evelyn Viola,
Christine Kiruthu-Kamamia,
Odala Sande,
Hannock Tweya,
Richard Anderson
Abstract:
eHealth has strong potential to advance HIV care in low- and middle-income countries. Given the sensitivity of HIV-related information and the risks associated with unintended HIV status disclosure, clients' privacy perceptions towards eHealth applications should be examined to develop client-centered technologies. Through focus group discussions with antiretroviral therapy (ART) clients from Ligh…
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eHealth has strong potential to advance HIV care in low- and middle-income countries. Given the sensitivity of HIV-related information and the risks associated with unintended HIV status disclosure, clients' privacy perceptions towards eHealth applications should be examined to develop client-centered technologies. Through focus group discussions with antiretroviral therapy (ART) clients from Lighthouse Trust, Malawi's public HIV care program, we explored perceptions of data security and privacy, including their understanding of data flow and their concerns about data confidentiality across several layers of data use. Our findings highlight the broad privacy concerns that affect ART clients' day-to-day choices, clients' trust in Malawi's health system, and their acceptance of, and familiarity with, point-of-care technologies used in HIV care. Based on our findings, we provide recommendations for building robust digital health systems in low- and middle-income countries with limited resources, nascent privacy regulations, and political will to take action to protect client data.
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Submitted 5 April, 2024;
originally announced April 2024.
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A Pilot Study of Sidewalk Equity in Seattle Using Crowdsourced Sidewalk Assessment Data
Authors:
Chu Li,
Lisa Orii,
Mikey Saugstad,
Stephen J. Mooney,
Yochai Eisenberg,
Delphine Labbé,
Joy Hammel,
Jon E. Froehlich
Abstract:
We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spati…
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We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spatial patterns of sidewalk quality in Seattle and their relationship to racial diversity, income level, built density, and transit modes. We close with a reflection on our approach, key limitations, and opportunities for future work.
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Submitted 5 October, 2022;
originally announced November 2022.
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Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Authors:
Di Jin,
Zhijing Jin,
Joey Tianyi Zhou,
Lisa Orii,
Peter Szolovits
Abstract:
Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract more readers. With no style-specific article-headline pair (only a standard headlin…
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Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates style-specific headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from the text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines surpasses that of the state-of-the-art summarization model by 9.68%, and even outperforms human-written references.
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Submitted 28 May, 2020; v1 submitted 4 April, 2020;
originally announced April 2020.