ORCID Profile
0000-0002-7831-2632
Current Organisations
Monash University
,
Keio University
,
Osaka Prefecture University
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Publisher: ACM
Date: 02-12-2020
Publisher: IEEE
Date: 03-2020
Publisher: Springer Science and Business Media LLC
Date: 23-06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Springer Science and Business Media LLC
Date: 21-06-2022
DOI: 10.1038/S41597-022-01383-6
Abstract: During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve in iduals’ experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing ersity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy.
Publisher: ACM
Date: 12-09-2016
Publisher: ACM
Date: 08-10-2018
Publisher: ACM
Date: 20-03-2017
Publisher: ACM
Date: 12-09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: MDPI AG
Date: 13-07-2023
DOI: 10.3390/INFORMATICS10030061
Abstract: Online knowledge-based peer-production communities, like question and answer sites (Q& A), often rely on gamification, e.g., through reputation points, to incentivize users to contribute frequently and effectively. These gamification techniques are important for achieving the critical mass that sustains a community and enticing new users to join. However, aging communities tend to build “poverty traps” that act as barriers for new users. In this paper, we present our investigation of 32 domain communities from Stack Exchange and our analysis of how different subjects impact the development of early user advantage. Our results raise important questions about the accessibility of knowledge-based peer-production communities. We consider the analysis results in the context of changing information needs and the relevance of Q& A in the future. Our findings inform policy design for building more equitable knowledge-based peer-production communities and increasing the accessibility to existing ones.
Publisher: ACM
Date: 29-04-2022
Publisher: ACM
Date: 08-10-2018
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Elsevier BV
Date: 10-2022
Publisher: ACM
Date: 08-10-2018
Publisher: ACM
Date: 06-02-2018
Publisher: ACM
Date: 11-09-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 09-09-2019
Publisher: ACM
Date: 27-06-2022
Publisher: ACM
Date: 02-05-2019
Publisher: ACM
Date: 08-05-2021
Publisher: Elsevier BV
Date: 07-2023
Publisher: ACM
Date: 08-10-2023
Publisher: ACM
Date: 09-09-2019
Publisher: ACM
Date: 21-09-2021
Publisher: JMIR Publications Inc.
Date: 27-10-2020
DOI: 10.2196/19874
Abstract: The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.
Publisher: Informa UK Limited
Date: 11-09-2020
Publisher: ACM
Date: 11-09-2022
Publisher: JMIR Publications Inc.
Date: 05-05-2020
Abstract: he use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. he aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. e developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. sing ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. nalysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.
Publisher: ACM
Date: 06-05-2021
Publisher: ACM
Date: 29-04-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 14-10-2023
Publisher: ACM
Date: 29-04-2022
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: ACM
Date: 19-04-2023
Publisher: ACM
Date: 25-04-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 21-09-2021
Publisher: The Royal Society
Date: 02-2021
DOI: 10.1098/RSOS.200589
Abstract: The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that in iduals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at osf.io/ytbcs . This preregistration was performed prior to data analysis.
Publisher: ACM
Date: 27-04-2022
Publisher: ACM
Date: 09-10-2023
Publisher: ACM
Date: 11-11-2014
Publisher: Elsevier BV
Date: 05-2022
Publisher: ACM
Date: 16-03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: ACM
Date: 08-10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 06-05-2017
Publisher: ACM
Date: 11-09-2017
Publisher: Springer Science and Business Media LLC
Date: 06-05-2022
DOI: 10.1007/S10916-022-01817-Z
Abstract: The World Health Organization (WHO) recommends a six-step hand hygiene technique. Although multiple studies have reported that this technique yields inadequate skin coverage outcomes, they have relied on manual labeling that provided low-resolution estimations of skin coverage outcomes. We have developed a computational system to precisely quantify hand hygiene outcomes and provide high-resolution skin coverage visualizations, thereby improving hygiene techniques. We identified frequently untreated areas located at the dorsal side of the hands around the abductor digiti minimi and the first dorsal interosseous. We also estimated that excluding Steps 3, 6R, and 6L from the six-step hand hygiene technique leads to cumulative coverage loss of less than 1%, indicating the potential redundancy of these steps. Our study demonstrates that the six-step hand hygiene technique could be improved to reduce the untreated areas and remove potentially redundant steps. Furthermore, our system can be used to computationally validate new proposed techniques, and help optimise hand hygiene procedures.
Publisher: Springer Science and Business Media LLC
Date: 04-01-2021
DOI: 10.1038/S41597-020-00784-9
Abstract: This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
Publisher: ACM
Date: 09-04-2031
No related grants have been discovered for Benjamin Tag.