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Browsing by Author "Zaman, Nohel"

Now showing 1 - 3 of 3
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    Injury prevention for older adults: A dataset of safety concern narratives from online reviews of mobility-related products
    Restrepo, Felipe; Mali, Namrata; Sands, Laura P.; Abrahams, Alan; Goldberg, David M.; White, Janay; Prieto, Laura; Ractham, Peter; Gruss, Richard; Zaman, Nohel; Ehsani, Johnathon P. (Elsevier, 2022-06)
    Older adults are among the fastest-growing demographic groups in the United States, increasing by over a third this past decade. Consequently, the older adult consumer prod-uct market has quickly become a multi-billion-dollar in-dustry in which millions of products are sold every year. However, the rapidly growing market raises the poten-tial for an increasing number of product safety concerns and consumer product-related injuries among older adults. Recent manufacturer and consumer injury prevention efforts have begun to turn towards online reviews, as these provide valuable information from which actionable, timely intelligence can be derived and used to detect safety concerns and prevent injury. The presented dataset contains 1966 curated online product reviews from consumers, equally distributed between safety concerns and non-concerns, pertaining to product categories typically intended for older adults. Identified safety concerns were manually sub-coded across thirteen dimensions designed to capture relevant aspects of the consumer's experience with the purchased product, facilitate the safety concern identification and sub-classification process, and serve as a gold-standard, balanced dataset for text classifier learning. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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    Online Review Analytics: New Methods for discovering Key Product Quality and Service Concerns
    Zaman, Nohel (Virginia Tech, 2019-07-09)
    The purpose of this dissertation intends to discover as well as categorize safety concern reports in online reviews by using key terms prevalent in sub-categories of safety concerns. This dissertation extends the literature of semi-automatic text classification methodology in monitoring and classifying product quality and service concerns. We develop various text classification methods for finding key concerns across a diverse set of product and service categories. Additionally, we generalize our results by testing the performance of our methodologies on online reviews collected from two different data sources (Amazon product reviews and Facebook hospital service reviews). Stakeholders such as product designers and safety regulators can use the semi-automatic classification procedure to subcategorize safety concerns by injury type and narrative type (Chapter 1). We enhance the text classification approach by proposing a Risk Assessment Model for quality management (QM) professionals, safety regulators, and product designers to allow them to estimate overall risk level of specific products by analyzing consumer-generated content in online reviews (Chapter 2). Monitoring and prioritizing the hazard risk levels of products will help the stakeholders to make appropriate actions on mitigating the risk of product safety. Lastly, the text classification approach discovers and ranks aspects of services that predict overall user satisfaction (Chapter 3). The key service terms are beneficial for healthcare providers to rapidly trace specific service concerns for improving the hospital services.
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    The Relationship between Nurses’ Training and Perceptions of Electronic Documentation Systems
    Zaman, Nohel; Goldberg, David M.; Kelly, Stephanie; Russell, Roberta S.; Drye, Sherrie L. (MDPI, 2021-01-01)
    Electronic documentation systems have been widely implemented in the healthcare field. These systems have become a critical part of the nursing profession. This research examines how nurses’ general computer skills, training, and self-efficacy affect their perceptions of using these systems. A sample of 248 nurses was surveyed to examine their general computer skills, self-efficacy, and training in electronic documentation systems in nursing programs. We propose a model to investigate the extent to which nurses’ computer skills, self-efficacy, and training in electronic documentation influence perceptions of using electronic documentation systems in hospitals. The data supports a mediated model in which general computer skills, self-efficacy, and training influence perceived usefulness through perceived ease of use. The significance of these findings was confirmed through structural equation modeling. As the electronic documentation systems are customized for every organization, our findings suggest value in nurses receiving training to learn these specific systems in the workplace or during their internships. Doing so may improve patient outcomes by ensuring that nurses use the systems consistently and effectively.