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1. Rural Resiliency Hubs: An Integrated, Community-Centered Approach to Addressing the Resiliency Divide through Rural Public Libraries

   Mardis, M.A. ( January 2022 , New Librarianship Symposium)

   Lankes, R. David (Ed.)

   Resilience is often treated as a single-dimension system attribute, or various dimensions of resilience are studied separately without considering multi-dimensionality. The increasing frequency of catastrophic natural or man-made disasters affecting rural areas demands holistic assessments of community vulnerability and assessment. Disproportionate effects of disasters on minorities, low-income, hard-to-reach, and vulnerable populations demand a community-oriented planning approach to address the “resilience divide.” Rural areas have many advantages, but low population density, coupled with dispersed infrastructures and community support networks, make these areas more affected by natural disasters. This paper will catalyze three key learnings from our current work in public librarians’ roles in disaster resiliency: rural communities are composed of diverse sub-communities, each which experiences and responds to traumatic events differently, depending on micro-geographic and demographic drivers. Rural citizens tend to be very self-reliant and are committed to strengthening and sustaining community resiliency with local human capital and resources. Public libraries are central to rural life, providing a range of informational, educational, social, and personal services, especially in remote areas that lack reliable access to community resources during disasters. Public libraries and their librarian leaders are often a “crown jewel” of rural areas’ community infrastructure and this paper will present a community-based design and assessment process for resiliency hubs located in and operated through rural public libraries. The core technical and social science research questions explored in the proposed paper are: 1) Who were the key beneficiaries and what did they need? 2) What was the process of designing a resiliency hub? 3) What did library resiliency hubs provide and how can they be sustained? This resiliency hub study will detail co-production of solutions and involves an inclusive collaboration among researchers, librarians, and community members to address the effects of cascading impacts of natural disasters. The novel co-design process detailed in the paper reflects an in-depth understanding of the complex interactions among libraries, residents, governments, and other agencies by collecting sociotechnical hurricane-related data for Calhoun County, Florida, USA, a region devastated by Hurricane Michael (2018) and hard-hit by Covid-19. We analyzed data from newly developed fusing algorithms and incorporating multiple communities and developed a framework and process to co-design resiliency hubs sited in public libraries. This research leverages a unique opportunity to library-centered policies and technologies to establish a new paradigm for developing disaster resiliency in rural settings. Public libraries serve a diverse population who will directly benefit from practical support tailored to their needs. The project will inform efficient plans to ensure that high-need groups are not isolated in disasters. The knowledge and insight gained from the resiliency hub design process will not only improve our understanding of emergency response operations, but also will contribute to the development of new disaster related policies and plans for public libraries, with a broader application to rural communities in many settings. 

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2. Rural Resiliency Hubs: An Integrated, Community-Centered Approach to Addressing the Resiliency Divide through Rural Public Libraries

   Mardis, Marcia A. ; Jones, Faye R. ; Ozguven, Eren E. ; Horner, Mark ; Piekalkiewicz, Ellen ; Pickett, Scott ; Mathias, John ; De Leon, Jessica ( January 2021 , New Librarianship Symposia)

   Lankes, R.David (Ed.)

   Resilience is often treated as a single-dimension system attribute, or various dimensions of resilience are studied separately without considering multi-dimensionality. The increasing frequency of catastrophic natural or man-made disasters affecting rural areas demands holistic assessments of community vulnerability and assessment. Disproportionate effects of disasters on minorities, low-income, hard-to-reach, and vulnerable populations demand a community-oriented planning approach to address the “resilience divide.” Rural areas have many advantages, but low population density, coupled with dispersed infrastructures and community support networks, make these areas more affected by natural disasters. This paper will catalyze three key learnings from our current work in public librarians’ roles in disaster resiliency: 1) rural communities are composed of diverse sub-communities, each which experiences and responds to traumatic events differently, depending on micro-geographic and demographic drivers; 2) public libraries are central to rural life, providing a range of informational, educational, social, and personal services, especially in remote areas that lack reliable access to community resources during disasters; and 3) rural citizens tend to be very self-reliant and are committed to strengthening and sustaining community resiliency with local human capital and resources. Public libraries and their librarian leaders are often a “crown jewel” of rural areas’ community infrastructure and this paper will present a community-based design and assessment process for resiliency hubs located in and operated through rural public libraries. The core technical and social science research questions explored in the proposed paper are: 1) Who were the key beneficiaries and what did they need? 2) What was the process of designing a resiliency hub? 3) What did library resiliency hubs provide and how can they be sustained? This resiliency hub study will detail co-production of solutions and involves an inclusive collaboration among researchers, librarians, and community members to address the effects of cascading impacts of natural disasters. The novel co-design process detailed in the paper reflects 1) an in-depth understanding of the complex interactions among libraries, residents, governments, and other agencies by collecting sociotechnical hurricane-related data for Calhoun County, Florida, USA, a region devastated by Hurricane Michael (2018) and hard-hit by Covid-19; 2) analyzed data from newly-developed fusing algorithms and incorporating multiple communities; and 3) co-designed resiliency hubs sited in public libraries. This research leverages a unique opportunity for the co-development of integrated library-centered policies and technologies to establish a new paradigm for developing disaster resiliency in rural settings. Public libraries serve a diverse population who will directly benefit from practical support tailored to their needs. The project will inform efficient plans to ensure that high-need groups are not isolated in disasters. The knowledge and insight gained from disseminating the study’s results will not only improve our understanding of emergency response operations, but also will contribute to the development of new disaster-related policies and plans for public libraries, with a broader application to rural communities in many settings. 

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3. Diversified caregiver input to upgrade the Young Children’s Participation and Environment Measure for equitable pediatric re/habilitation practice

   https://doi.org/10.1186/s41687-023-00627-2  

   Villegas, Vivian C. ; Bosak, Dianna L. ; Salgado, Zurisadai ; Phoenix, Michelle ; Parde, Natalie ; Teplicky, Rachel ; Khetani, Mary A. ; High Value Early Intervention Research Group ; Kuznicki, L. ; Pedrow, A. ; et al ( August 2023 , Journal of Patient-Reported Outcomes)

   Practitioner and family experiences of pediatric re/habilitation can be inequitable. The Young Children’s Participation and Environment Measure (YC-PEM) is an evidence-based and promising electronic patient-reported outcome measure that was designed with and for caregivers for research and practice. This study examined historically minoritized caregivers’ responses to revised YC-PEM content modifications and their perspectives on core intelligent virtual agent functionality needed to improve its reach for equitable service design.

   Caregivers were recruited during a routine early intervention (EI) service visit and met five inclusion criteria: (1) were 18 + years old; (2) identified as the parent or legal guardian of a child 0–3 years old enrolled in EI services for 3 + months; (3) read, wrote, and spoke English; (4) had Internet and telephone access; and (5) identified as a parent or legal guardian of a Black, non-Hispanic child or as publicly insured. Three rounds of semi-structured cognitive interviews (55–90 min each) used videoconferencing to gather caregiver feedback on their responses to select content modifications while completing YC-PEM, and their ideas for core intelligent virtual agent functionality. Interviews were transcribed verbatim, cross-checked for accuracy, and deductively and inductively content analyzed by multiple staff in three rounds.

   Eight Black, non-Hispanic caregivers from a single urban EI catchment and with diverse income levels (Mdn = $15,001–20,000) were enrolled, with children (M = 21.2 months,SD = 7.73) enrolled in EI. Caregivers proposed three ways to improve comprehension (clarify item wording, remove or simplify terms, add item examples). Environmental item edits prompted caregivers to share how they relate and respond to experiences with interpersonal and institutional discrimination impacting participation. Caregivers characterized three core functions of a virtual agent to strengthen YC-PEM navigation (read question aloud, visual and verbal prompts, more examples and/or definitions).

   Results indicate four ways that YC-PEM content will be modified to strengthen how providers screen for unmet participation needs and determinants to design pediatric re/habilitation services that are responsive to family priorities. Results also motivate the need for user-centered design of an intelligent virtual agent to strengthen user navigation, prior to undertaking a community-based pragmatic trial of its implementation for equitable practice.

    

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4. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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5. Applications and User Perceptions of Smart Glasses in Emergency Medical Services: Semistructured Interview Study

   https://doi.org/10.2196/30883  

   Zhang, Zhan ; Joy, Karen ; Harris, Richard ; Ozkaynak, Mustafa ; Adelgais, Kathleen ; Munjal, Kevin ( January 2022 , JMIR Human Factors)

   Background Smart glasses have been gaining momentum as a novel technology because of their advantages in enabling hands-free operation and see-what-I-see remote consultation. Researchers have primarily evaluated this technology in hospital settings; however, limited research has investigated its application in prehospital operations. Objective The aim of this study is to understand the potential of smart glasses to support the work practices of prehospital providers, such as emergency medical services (EMS) personnel. Methods We conducted semistructured interviews with 13 EMS providers recruited from 4 hospital-based EMS agencies in an urban area in the east coast region of the United States. The interview questions covered EMS workflow, challenges encountered, technology needs, and users’ perceptions of smart glasses in supporting daily EMS work. During the interviews, we demonstrated a system prototype to elicit more accurate and comprehensive insights regarding smart glasses. Interviews were transcribed verbatim and analyzed using the open coding technique. Results We identified four potential application areas for smart glasses in EMS: enhancing teleconsultation between distributed prehospital and hospital providers, semiautomating patient data collection and documentation in real time, supporting decision-making and situation awareness, and augmenting quality assurance and training. Compared with the built-in touch pad, voice commands and hand gestures were indicated as the most preferred and suitable interaction mechanisms. EMS providers expressed positive attitudes toward using smart glasses during prehospital encounters. However, several potential barriers and user concerns need to be considered and addressed before implementing and deploying smart glasses in EMS practice. They are related to hardware limitations, human factors, reliability, workflow, interoperability, and privacy. Conclusions Smart glasses can be a suitable technological means for supporting EMS work. We conclude this paper by discussing several design considerations for realizing the full potential of this hands-free technology. 

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