Proposing and Empirically Investigating a Mobile-Based Outpatient Healthcare Service Delivery Framework Using Stimulus–Organism–Response Theory

Chudhery, Muhammad Adnan Zahid; Safdar, Sarah; Huo, Jiazhen; Rehman, Hajira; Rafique, Raza

doi:10.1109/tem.2021.3081571

Cited by 21 publications

(9 citation statements)

References 78 publications

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“…, 2020; Zhang et al. , 2021) and health care services (Chudhery et al. , 2021; Suess and Mody, 2018) but only few examples illustrate the use of S-O-R logic on EE (Duong, 2023).…”

Section: Conceptual Background and Preliminary Modelingmentioning

confidence: 99%

The entrepreneurial mindset in entrepreneurship education: what can we learn from S-O-R models?

Larsen,

Blenker,

Neergaard

2023

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PurposeThe aim of this paper is to examine the usefulness of the stimulus-organism-response (S-O-R) model for systematizing and further exploring the knowledge of the role of entrepreneurship education (EE) in fostering students' entrepreneurial mindset (EM). Current research studying the EM in an educational setting often fails to conceptualize this mindset and its attributes rigorously and to include epistemological considerations regarding how the authors can know whether a person has developed an EM or not.Design/methodology/approachThe authors test the S-O-R model by combining it with a critical realist approach to develop a conceptual model for understanding the EM and its development in an educational setting.FindingsThe authors identify eight psychological constructs that represent markers of the EM. The authors further surface the multi-causal characteristic of learning in EE. The authors' model cannot be applied to detect causal relationships, but it is useful in exploring potential causal tendencies and plausible explanations of the mechanisms and circumstances of EE, which may, in concert, potentially foster the psychological attributes associated with an EM.Originality/valueThe paper addresses the often-overlooked epistemological considerations related to how the authors can know anything about psychological and theoretical constructs, such as the EM. A conscious discussion about how the authors can develop knowledge and insight about the EM and how individuals develop it is critical to justify a continued focus on the EM in EE.

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“…, 2020; Zhang et al. , 2021) and health care services (Chudhery et al. , 2021; Suess and Mody, 2018) but only few examples illustrate the use of S-O-R logic on EE (Duong, 2023).…”

Section: Conceptual Background and Preliminary Modelingmentioning

confidence: 99%

The entrepreneurial mindset in entrepreneurship education: what can we learn from S-O-R models?

Larsen,

Blenker,

Neergaard

2023

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“…Recently, a convolutional neural network has been applied increasingly in the use of computer vision for biomedical image analysis [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. However, the precise segmentation of images is difficult for low-contrast blood vessels in the retina and requires access to a huge database for training, while an unsupervised learning technique may be available in which such extensive training is not required.…”

Section: Introductionmentioning

confidence: 99%

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

et al. 2022

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Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

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“…Indeed, leveraging digital health technologies plays a central role in responding to Covid-19 [7], [8]. Further demonstrating their significance, there are over 40 online healthcare service providers in China [9].…”

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confidence: 99%

“…In health care, population ageing places increased pressure on primary and specialist care resources across countries [16]. In principle, DHPs can help mitigate this challenge [9]. Indeed, DHPs have the potential to facilitate independent living [17], which is a key concern in the elderly population [18].…”

mentioning

confidence: 99%

“…While it is increasingly acknowledged that "the elderly" is a heterogeneous group who should not be viewed as "laggards" responding negatively to new technology [25], prior research has provided few insights into attitudes to DHPs let alone adoption and postadoption usage patterns. Instead, extant studies have focused either on mHealth adoption antecedents among physicians [9], [26], [27], or on factors influencing the propensity of younger individuals to use DHPs, such as online healthcare communities (which provide users with health information and/or peer-to-peer communication) [28].…”

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confidence: 99%

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Older Individuals and Digital Healthcare Platforms: Usage Motivations and the Impact of Age on Postadoption Usage Patterns

Frishammar

Essén

Simms

et al. 2023

IEEE Trans. Eng. Manage.

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Digital healthcare platforms have enabled patients to receive healthcare in ways that were impossible previously-for example, by providing a "safer" way to meet, as underscored by the Covid-19 pandemic. This article investigates whether older and younger primary care users display behavioral differences on digital healthcare platforms. The article adopts a mixed-method approach in which one-way ANOVA analysis on a sample of 152 000 patient journeys was combined with qualitative interview data. The findings highlight significant differences in usage between elderly and younger patients. The elderly spends more time during use-for example, during anamnesis, onboarding, and in queues. We also outline how the key antecedent factors that are most central to platform usage, such as perceived usefulness, perceived ease of use, digital maturity, and trust, play out in the elderly user context. The study contributes to the nascent literature on digital healthcare platforms and the postadoption usage of information and communication technologies by the elderly. The article also outlines research implications in the area of DHPs and mHealth for elderly users, and it discusses the practical implications for both platform owners and healthcare professionals, where platform design and information management are particularly important for elderly users.

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Proposing and Empirically Investigating a Mobile-Based Outpatient Healthcare Service Delivery Framework Using Stimulus–Organism–Response Theory

Cited by 21 publications

References 78 publications

The entrepreneurial mindset in entrepreneurship education: what can we learn from S-O-R models?

The entrepreneurial mindset in entrepreneurship education: what can we learn from S-O-R models?

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Older Individuals and Digital Healthcare Platforms: Usage Motivations and the Impact of Age on Postadoption Usage Patterns

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