Background: Presenteeism when ill in healthcare personnel (HCP) can contribute to the spread of respiratory illness among HCP and patients. However, during the COVID-19 pandemic and now, there are substantial challenges preventing HCP from staying home when ill. We examined these challenges using the Systems Engineering Initiative for Patient Safety (SEIPS) framework. Method: As part of a larger anonymous electronic survey between 3/11/2022 and 4/12/2022 at an academic tertiary referral center, in inpatient and ambulatory settings where respondents were asked to describe factors impacting presenteeism when ill, we analyzed free-text responses using the SEIPS categories of tasks, tools/technology, person, organization, and physical environment. Result: 522 comments were received in response to the open-ended survey question asking individuals to describe any factors that would assist them in remaining home and/or help them get tested for COVID-19 when they have symptoms of a respiratory illness; 21 were excluded due to absent or incomplete response. Of the remaining responses (N = 501, Figure 1), 82% were associated with a single SEIPS component such as organization (N = 409), while other responses discussed factors that involved two SEIPS components, in no particular order (N = 92). A majority of the responses (N = 324, 55%) reported organizational barriers, frequently citing a strict sick call-in policy as well as a lack of protected time-off for COVID-19 testing or related absences. The next two most commonly identified components were physical environment (N= 88, 15%) and tasks (N = 72, 12%), mentioning barriers such as far distances to testing centers and prolonged waiting periods for testing Results: The person and tools/technology components were less commonly identified, with a frequency of 9% each. Conclusion: A number of systems level factors were identified that may impact the ability of HCP to stay home when ill. Interventions to help overcome HCP perceived barriers to staying home when experiencing respiratory symptoms should focus on the policies and practices within an organization. Communication from leadership should support staying home with respiratory symptoms by creating plans for coverage and back up consistently across all employee types in direct care.

Background: Amid the COVID-19 pandemic, healthcare systems were stretched thin, with staffing shortages posing substantial challenges. Limiting spread of COVID-19 among healthcare professionals (HCP) is paramount to preventing exacerbation of such shortages, but strategies are highly dependent on HCP self-screening for symptoms and isolating when present. We examined HCP perceptions of barriers and factors that facilitate staying home when experiencing respiratory symptoms. Methods: At an academic tertiary-care referral center, in inpatient and ambulatory settings, we conducted an anonymous electronic survey between March 11, 2022, and April 12, 2022. Using logistic regression analysis, we analyzed predictors of employees reporting to work with respiratory symptoms using STATA and SAS software. Results: In total, 1,185 individuals including 829 clinical staff and 356 nonclinical staff responded to the survey. When excluding participants who reported working “remotely” (N = 381) and those who reported being unsure of whether they had worked with symptoms (N = 14), the prevalence of working with respiratory symptoms was 63%. There was no significant difference between clinical and nonclinical staff (OR, 1.1; 95% CI, 0.8–1.5; P = .60). Increasing number of years of service was protective against working with symptoms, achieving statistically significance in multivariable analysis after 16 years. Compared to those having worked <1 year, the odds ratios of working with symptoms were 0.32 (95% CI, 0.16–0.65; P = .002), 0.33 (95% CI, 0.15–0.74; P = .007), and 0.32 (95% CI, 0.13–0.79; P = .007) for those working 16–20 years, 21–25 years, and ≥26 years, respectively. More than half of HCP who worked with symptoms identified being understaffed (56.9%), having mild symptoms (55.3%), and sense of responsibility (55.1%) as reasons to work with respiratory symptoms. The following barriers, or reasons to work with symptoms, were more commonly identified as significant by those who worked with symptoms compared to those who did not: being understaffed (OR, 1.87; 95% CI, 1.35–2.58; P ≤ .001), having mild symptoms (OR, 1.96; 95% CI, 1.42–2.71; P < .001), and lack of support from management (OR, 1.84; 95% CI, 1.07–3.18; P = .03). Conclusions: Working with respiratory symptoms is prevalent in clinical and nonclinical HCP. Those with fewer years of work experience appear to be more susceptible to misconceptions and pressures to work despite respiratory symptoms. Messaging should stress support from leadership and the significance of even mild respiratory symptoms and should emphasize responsibility to patients and colleagues to stay home with respiratory symptoms. Strategies to ensure adequate staffing and sick leave may also be high yield.

Disclosures: None

Risk attitudes implied by valuations of risk-increasing assets depart markedly from those implied by valuations of risk-reducing assets. For instance, many are unwilling to pay the expected value for a risky asset or for its perfect hedge. Although nearly every theory of risk preference (and logic) demands a negative correlation between valuations of bets and hedges, we observe positive correlations. This inconsistency is difficult to expunge.

OBJECTIVES/GOALS: The Virtual CTSA Visiting Scholars Program allows KL2 scholars to serve as visiting professors at host CTSA consortium institutions. This program facilitates connections with faculty outside scholars home institutions, as well as fostering collaboration among different CTSA hubs. Stanford aimed to incorporate participants into its KL2 program. METHODS/STUDY POPULATION: Stanford is one of 57 host institutions for the Virtual CTSA Visiting Scholars Program. The program includes a virtual meeting between each participant and a chosen faculty at the host institution. Program participants also give a virtual Grand Rounds lecture, open to the entire CTSA consortium. As well as encouraging Spectrum KL2 Scholars to participate in the virtual exchange, the Spectrum KL2 program incorporated the visiting scholars into weekly activities over the course of 3 months. Visiting scholars were invited to participate virtually in KL2 educational sessions, which provide career development training and mentoring. As meetings transitioned to in-person, KL2 scholars were allowed to attend virtually when needed. Hybrid in-person sessions were also conducive to participation by visiting scholars. RESULTS/ANTICIPATED RESULTS: Stanfords Spectrum KL2 program was virtual (Zoom) for academic year 2020-2021 and included 1:1 mentoring sessions, weekly career development seminars, and 1:1 peer virtual lunches to integrate the 9 junior faculty scholars. Visiting scholars joined the weekly Zoom meetings when their schedules allowed. KL2 faculty mentored visiting scholars for 3 months, exchanging ideas and forming collaborations. Each visiting participant was paired with a KL2 Scholar, who provided 1:1 peer mentorship. In addition, visiting scholars presented a work-in-progress seminar, to obtain feedback before the more formal Grand Rounds lecture. For academic year 2021-2022, Spectrum KL2 meetings include virtual and hybrid in-person sessions, allowing visiting scholars to join by Zoom during the 3-month virtual exchange program. DISCUSSION/SIGNIFICANCE: What set Stanfords Virtual CTSA Visiting Scholars Program apart was faculty engagement and mentorship provided to visiting participants. By incorporating visiting scholars virtually into the ongoing KL2 education program, participants could engage fully with mentors and scholars, even surpassing opportunities of pre-pandemic on-site visits.

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.