Gender Bias in Research

Upchurch, Meg

doi:10.1002/9781119315063.ch7

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…97 Another term for this could be Ageism which is a form of discrimination and discourages people from adopting technologies if they are told that this is specifically for frail, vulnerable and old aged people. 123 Awareness, experience or low interaction with technology, ownership of devices (early adoption), and personality can also impact adoption of technology. 73,89 It was found that males were more aware of IoT and the term "smart home" than females.…”

Section: Stigma Social Pressure Awareness and Other Issuesmentioning

confidence: 99%

“…They need be large and aligned to the population they are designed for. 123 Data set accountability is also essential when determining what is going to be used in an application. 72 Another example is of patients with dementia where providing care varies on an individual basis.…”

Section: Design and Data Biasmentioning

confidence: 99%

“…A behavior pattern based on an incomplete data set or on average values may not produce expected results and undermine the care. 123 Data can be collected from real environments and shared with hospitals and researchers over years to collect real data and experiment research on that. 123…”

Section: Design and Data Biasmentioning

confidence: 99%

“…123 Data can be collected from real environments and shared with hospitals and researchers over years to collect real data and experiment research on that. 123…”

Section: Design and Data Biasmentioning

confidence: 99%

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Ethics and acceptance of smart homes for older adults

Pirzada

Wilde

Doherty

et al. 2021

Informatics for Health and Social Care

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Societal challenges associated with caring for the physical and mental health of older adults worldwide have grown at an unprecedented pace, increasing demand for health-care services and technologies Despite the development of several assistive systems tailored to older adults, the rate of adoption of health technologies is low. This review discusses the ethical and acceptability challenges resulting in low adoption of health technologies specifically focused on smart homes for older adults. The findings have been structured in two categories: Ethical Considerations (Privacy, Social Support, and Autonomy) and Technology Aspects (User Context, Usability, and Training). The findings conclude that older adults community is more likely to adopt assistive systems when four key criteria are met. The technology should: be personalized toward their needs, protect their dignity and independence, provide user control, and not be isolating. Finally, we recommend researchers and developers working on assistive systems to: (1) provide interfaces via smart devices to control and configure the monitoring system with feedback for the user, (2) include various sensors/devices to architect a smart home solution in a way that is easy to integrate in daily life, and (3) define policies about data ownership.

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Section: Stigma Social Pressure Awareness and Other Issuesmentioning

confidence: 99%

Section: Design and Data Biasmentioning

confidence: 99%

Section: Design and Data Biasmentioning

confidence: 99%

“…123 Data can be collected from real environments and shared with hospitals and researchers over years to collect real data and experiment research on that. 123…”

Section: Design and Data Biasmentioning

confidence: 99%

See 2 more Smart Citations

Ethics and acceptance of smart homes for older adults

Pirzada

Wilde

Doherty

et al. 2021

Informatics for Health and Social Care

View full text Add to dashboard Cite

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“…Similarly, poor‐fitting personal protective equipment (PPE) reduced nurses' protection from coronavirus disease 2019 (Covid‐19) (Fidler, 2020) and stab vests provide less protection and more discomfort for female police officers due to being modeled on the male body form (Niemczyk et al, 2020). It should be noted that there may be occasions when biases in data collection may also be toward women in areas where women hold the majority population; however, it is the case that generally the bias is in favor of males, especially in engineering and design‐based disciplines (Criado‐Perez, 2019; Upchurch, 2020).…”

Section: Introductionmentioning

confidence: 99%

How can human factors close the gender data gap?

Parnell,

Plant

2023

Hum Ftrs & Erg Mfg Svc

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This commentary paper will describe how the discipline of human factors and ergonomics (HFE) can help to close the gender data gap, which is prevalent across many domains and arises due to a lack of data capturing female metrics and viewpoints. HFE is a domain‐independent discipline that seeks to understand human performance and well‐being with respect to the interactions that humans engage in and the environments that they inhabit. HFE therefore presents an opportunity to understand how gender influences human performance, effective design, social interactions, and environmental factors. This paper argues that a sociotechnical systems approach is essential when reviewing equality, diversity, and inclusivity issues, without which attempts to close the gender data gap will not go far enough. Following the sociotechnical systems approach in HFE, the micro‐, meso‐, and macro‐levels of system design with respect to closing the gender data gap are reviewed. We discuss these issues in relation to a case study example of a crash test dummy. A checklist approach for researchers is presented, which identifies key questions that prompt where gender should be considered in the research process across these levels of sociotechnical systems.

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