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Objectives/Goals: To explore the caregivers’ lived experiences related to facilitators of and barriers to effective primary care or neurology follow-up for children discharged from the pediatric emergency department (PED) with headaches. Methods/Study Population: We used the descriptive phenomenology qualitative study design to ascertain caregivers’ lived experiences with making follow-up appointments after their child’s PED visit. We conducted semi-structured interviews with caregivers of children with headaches from 4 large urban PEDs over HIPAA-compliant Zoom conferencing platform. A facilitator/co-facilitator team (JH and SL) guided all interviews, and the audio of which was transcribed using the TRINT software. Conventional content analysis was performed by two coders (JH and AS) to generate new themes, and coding disputes were resolved by team members using Atlas TI (version 24). Results/Anticipated Results: We interviewed a total of 11 caregivers (9 mothers, 1 grandmother, and 1 father). Among interviewees, 45% identified as White non-Hispanic, 45% Hispanic, 9% as African-American, and 37% were publicly insured. Participants described similar experiences in obtaining follow-up care that included long waits to obtain neurology appointments. Participants also described opportunities to overcome wait times that included offering alternative healthcare provider types as well as telehealth options. Last, participants described desired action while awaiting neurology appointments such as obtaining testing and setting treatment plans. Discussion/Significance of Impact: Caregivers perceived time to appointment as too long and identified practical solutions to ease frustrations while waiting. Future research should explore sharing caregiver experiences with primary care providers, PED physicians, and neurologists while developing plans to implement caregiver-informed interventions.
This chapter gives an overview of data-driven methods applied to turbulence closure modeling for coarse graining. A non-exhaustive introduction of the various data-driven approaches that have been used in the context of closure modeling is provided which includes a discussion of model consistency, which is the ultimate indicator of a successful model, and other key concepts. More details are then presented for two specific methods, one a neural-network representative of nontransparent black-box approaches and one specific type of evolutionary algorithm representative of transparent approaches yielding explicit mathematical expressions. The importance of satisfying physical constraints is emphasized and methods to choose the most relevant input features are suggested. Several recent applications of data-driven methods to subgrid closure modeling are discussed, both for nonreactive and reactive flow configurations. The chapter is concluded with current trends and an assessment of what can be realistically expected of data-driven methods for coarse graining.
Highly portable and accessible MRI technology will allow researchers to conduct field-based MRI research in community settings. Previous guidance for researchers working with fixed MRI does not address the novel ethical, legal, and societal issues (ELSI) of portable MRI (pMRI). Our interdisciplinary Working Group (WG) previously identified 15 core ELSI challenges associated with pMRI research and recommended solutions. In this article, we distill those detailed recommendations into a Portable MRI Research ELSI Checklist that offers practical operational guidance for researchers contemplating using this technology.
The newly designed HVE gas interface enables the AMS measurement of carbon samples in CO2 form. The CO2, e.g. resulting from the sample combustion in an elemental analyzer, is adsorbed in a zeolite trap and subsequently transferred to a motor-driven syringe. Once diluted with He, the gas mixture is transferred into the ion source of the AMS system. A carbon ion beam is formed in the ion source and mass-analyzed by the AMS system, resulting in 13C/12C and 14C/12C isotopic ratios. The HVE gas interface features two traps and two syringes to maximize the sample throughput, which results in more than 10 samples per hour. The first performance results of CO2 gas sample AMS measurements that were performed with the HVE gas interface in combination with the HVE 210 kV AMS system are presented in this paper. The measurements show that the gas interface contribution to the 14C/12C background is in the 10–15 level and to the precision is at or below 1%.
Identifying long-term care facility (LTCF)-exposed inpatients is important for infection control research and practice, but ascertaining LTCF exposure is challenging. Across a large validation study, electronic health record data fields identified 76% of LTCF-exposed patients compared to manual chart review.