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Aligning job descriptions at Duke University Schools of Medicine and Nursing with the Joint Task Force for Clinical Trial Competencies (JTFCTC) spurred additional related activities--namely, establishing professional pathways and career ladders for clinical research professional (CRP) positions. CRPs leave Academic Medical Centers for many reasons, but lack of career advancement opportunities is one of the top observations. Duke developed a competency-based tiering system for CRP job classifications. This process of Tier Advancement allows CRPs to advance their tier through demonstration of knowledge, skills, and abilities in a variety of clinical research competencies. Tier Advancement is point-based and permits CRPs to advance within a single job classification, removing the need for onerous reclassification. The point-based nature of the system allows flexibility, in that CRPs can advance by demonstrating a limited set of in-depth specialized competencies or a breadth of responsibilities across a wide array of competencies. Tier Advancement has provided benefit to individual employees by offering an opportunity for self-driven career development. It has also benefited the institution by increasing CRP retention rates and allowed Duke to better understand current competency levels of its workforce.

OBJECTIVES/GOALS: Identify causes for clinical research professional turnover Define data collection methods for exit interviews Provide institutions with resources to collect and analyze exit interviews Employ strategies to maximize the impact of exit interviews on retention METHODS/STUDY POPULATION: The Clinical Research Professional Taskforce (CRPT) exit interview Subgroup has met monthly since January 2023. Action items were agreed to and minutes were kept and reviewed at subsequent virtual working meetings. All members were given opportunity to speak and contribute. After a landscape analysis, conducted via survey, five institutions agreed to provide examples of their exit interview questions. Members spoke at length about goals, methods, collection techniques, institutional involvement, lessons learned and practical applications that could become best practices. RESULTS/ANTICIPATED RESULTS: The Subgroup aggregated all questions into categories and developed sample questions incorporating all data without using any word for word. In order to allow for quantitative assessment and standardized reporting the Subgroup formulated questions to be responded to utilizing a Likert scale with free text fields for select questions where further information is needed. The Subgroup developed best practices describing decision-making metrics, understanding reasons for turnover and reporting data back to leadership. Practical aspects such as method and time of survey collection, anonymity, and training staff are also included. DISCUSSION/SIGNIFICANCE: We are hopeful that sample questions and best practices will be helpful and widely utilized. Understanding the causes and impacts of CRP turnover are critical to meeting the current needs of clinical research. Further work is being done to calculate the cost of turnover to make the business case.

OBJECTIVES/GOALS: Physicians can request the clinical use of investigational products for their patients through an FDA pathway called Expanded Access (EA). Most evaluations of EA focus on the FDA submission only. We sought to evaluate these requests through the full academic medical center process. METHODS/STUDY POPULATION: Through the Transforming Expanded Access to Maximize Support and Study grant, we reviewed regulatory records for single-patient EA requests at four institutions (Duke University, University of Rochester, University of Michigan, and University of Texas Southwestern) which occurred between June 1, 2021 and February 28, 2023. Key data was collected, including the investigational product requested, submission and approval dates, urgency of request, and indication for treatment. Descriptive statistics were performed with Microsoft Excel. RESULTS/ANTICIPATED RESULTS: A total of 405 EA requests were identified, of which 319 (78.8%) were for drugs, 59 (14.6%) for biologics, and 27 (6.7%) for medical devices. The majority were characterized as non-emergency (60.7%), but the proportion of emergency to non-emergency cases varied considerably when stratified by year, with a peak in emergency cases in 2020. The most common products included therapies for COVID-19 and Mpox. Median time to obtain all approvals for treatment was 7 days for emergency cases and 28 days for non-emergency. The FDA review took the least time, with a median of 1 day in non-emergency cases. Full board approval from an institutional review board in non-emergency cases was 7 days. DISCUSSION/SIGNIFICANCE: These results generally align with previous reports on EA submissions received by the FDA. The timelines for the EA process represent an important benchmark both for treatment planning and institutional improvement.

Over the past 7 years, Duke has implemented competency-based job classifications for clinical research professionals (CRPs) with a defined pathway for career advancement. The workforce is defined specifically as the collection of staff employed across the clinical research enterprise to operationalize clinical research and human participatory protocols through the hands-on conduct of protocol activities including participant enrollment, regulatory coordination, study documentation, data collection and management, and sponsor engagement. The competency framework for this critical workforce laid the foundation for a centrally developed on-demand onboarding program at Duke. The self-paced program is designed to engage learners through competency-based learning modules, guided mentor/manager discussions, and applied learning activities. Consisting of an initial E-Learning orientation to clinical research at Duke, called Express Start, followed by a 90-day role-based Onboarding Learning Plan, our onboarding program includes training in foundational pre-defined core competency areas and customizable learning paths. Associated Engagement Activity Packets for many clinical research competencies encourage mentor and/or manager involvement and hands-on learning for the employee through suggested enrichment activities. The program has been widely adopted for CRPs within the Duke University Schools of Medicine and Nursing, and newly hired CRPs and their managers have expressed satisfaction with these centrally offered tools. In this paper, we describe the methods used to develop and implement our competency-based onboarding program. We will share an evaluation of the program and planned next steps for expanding the suite of onboarding resources.

Study techniques that students report using are often ineffective. In this project, text messaging supported students utilizing evidence-based approaches -- low-stakes practice testing, distributed practice, and interleaved practice. Students enrolled in an undergraduate general chemistry course were divided into two groups through matched random assignment. Both participation and performance in the text messaging quizzes correlated to performance in the course. Specifically, performance and participation in the text messaging quizzes, along with class participation, accounted for 67% of the variability in the overall final grades.

Students often report using ineffective study techniques. This project encouraged practice testing, distributed practice, interleaved practice, and metacognitive accuracy to increase learning in an undergraduate general chemistry course. Overall, the purpose of this project was to use text messaging to support evidence-based study practices. Students were provided 20 questions a week via text message and answered four questions per day (Monday through Friday). Both before and after the exam, students rated how well they thought they performed on the exam. Generally, participating in answering practice questions, the accuracy of their quiz question responses, the ability to gauge one's knowledge, and class participation were significant predictors of course scores and subsequent chemistry course scores. Therefore, the use of text messaging to support practice testing, distributed practice, interleaved practice, and metacognition is one way to help students improve learning as measured by final course grades.

OBJECTIVES/GOALS: The FDA allows physicians to request clinical use of investigational drugs, biologics, and devices for patients with no satisfactory treatment options through a pathway called Expanded Access (EA). TEAMSS (Transforming Expanded Access to Maximize Support and Study) sought to examine single-patient cases to better characterize these patients. METHODS/STUDY POPULATION: We prospectively collected data on requests for single-patient EA at any one of the four collaborating TEAMSS institutions (Duke University, University of Rochester, University of Michigan, and University of Texas Southwestern) between September 1, 2021 and February 28, 2023. Regulatory and health records were reviewed for past cases that occurred between June 1, 2018 and August 31, 2021. Descriptive statistics were performed on data from the submission process, the patient demographics, the indication for treatment, and patient health status over time. RESULTS/ANTICIPATED RESULTS: The patient population was representative with respect to the largest racial groups (69.3% White / 13.0% Black or African American) and legal sex (51.3% male / 48.7% female). All ages were represented, with overrepresentation of those 60-70 years old (16.8%) and under 10 (14.8%). Patients were most often treated for infectious diseases (44.2%) or oncologic conditions (39.0%). Those who received more than one dose stayed on treatment for 76 days (median) and up to 1427 days (maximum). At the end of study, 53.9% had completed treatment as planned, moved to commercial product, or continued treatment. Death, disease progression, or failure to respond occurred for 31.9% of patients. DISCUSSION/SIGNIFICANCE: The population that receives Expanded access treatments is heterogeneous in both demographics and medical conditions. Some successful treatments are continued for years. Many patients complete their treatment, and a minority experience death or disease progression during treatment.

Measurements of Pollution In The Troposphere (MOPITT) is an instrument on NASA's Terra satellite that has measured tropospheric carbon monoxide (CO) from early 2000 to the present day. Validation of data from satellite instruments like MOPITT is often conducted using ground-based measurements to ensure the continued accuracy of the space-based instrument's measurements and its scientific results. Previous MOPITT validation studies generally found a larger bias in the MOPITT data poleward of 60∘ N. In this study, we use data from 2006 to 2019 from the Bruker IFS 125HR Fourier Transform Infrared spectrometer (FTIR) located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada, to validate the MOPITT version 8 (V8) retrievals. These comparisons utilize mid- and near-infrared FTIR measurements made as part of the Network for the Detection for Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), respectively. All MOPITT version 8 retrievals within a radius of 110 km (1∘) from the PEARL Ridge Laboratory and within a 24 h time interval are used in this validation study. MOPITT retrieval products include those from the near-infrared (NIR) channel, the thermal infrared (TIR) channel, and a joint product from the thermal and near-infrared (TIR–NIR) channels. Each channel's detector has 4 pixels. We calculated the MOPITT pixel-to-pixel biases for each pixel, which were found to vary based on the season and surface type (land or water). The systematic bias for pixel 1 over land is larger than that for other pixels, which can reach up to 20 ppb. We use a small-region approximation method to find filtering criteria. We then apply the filters to the MOPITT dataset to minimize the MOPITT pixel bias and the number of outliers in the dataset. The sensitivity of each MOPITT pixel and each product is examined over the Canadian high Arctic. We then follow the methodologies recommended by NDACC and TCCON for the comparison between the FTIR and satellite total column retrievals. MOPITT averaging kernels are used to weight the NDACC and TCCON retrievals and take into account the different vertical sensitivities between the satellite and PEARL FTIR measurements. We use a modified Taylor diagram to present the comparison results from each pixel for each product over land and water with NDACC and TCCON measurements. Our results show overall consistency between MOPITT and the NDACC and TCCON measurements. When compared to the FTIR, the NIR MOPITT retrievals have a positive bias of 3 %–10 % depending on the pixel. The bias values are negative for the TIR product, with values between −5 % and 0 %. The joint TIR–NIR products show differences of −4 % to 7 %. The drift in MOPITT biases (in units of % yr−1) relative to NDACC and TCCON varies by MOPITT data product. In the NIR, drifts vs. TCCON are smaller than those vs. NDACC; however, this scenario is reversed for the MOPITT TIR and joint TIR–NIR products. Overall, this study aims to provide detailed validation for MOPITT version 8 measurements in the Canadian high Arctic.

AirCore in situ vertical profiles sample the atmosphere from near the surface to the lower stratosphere, making them ideal for the validation of satellite tropospheric trace gas data. Here we present intercomparison results of AirCore carbon monoxide (CO) measurements with respect to retrievals from MOPITT (Measurements of Pollution In The Troposphere; version 8) and TROPOMI (TROPOspheric Monitoring Instrument), on board the NASA Terra and ESA Sentinel 5-Precursor satellites, respectively. Mean MOPITT/AirCore total column bias values and their standard deviation (0.4 ± 5.5, 1.7 ± 5.6, and 0.7 ± 6.0 for MOPITT thermal-infrared, near-infrared, and multispectral retrievals, respectively; all in %) are similar to results obtained in MOPITT/NOAA aircraft flask data comparisons from this study and from previous validation efforts. MOPITT CO retrievals are systematically validated using in situ vertical profiles from a variety of aircraft campaigns. Because most aircraft vertical profiles do not sample the troposphere's entire vertical extent, they must be extended upwards in order to be usable in validation. Here we quantify, for the first time, the error introduced in MOPITT CO validation by the use of shorter aircraft vertical profiles extended upwards by analyzing validation results of MOPITT with respect to full and truncated AirCore CO vertical profiles. Our results indicate that the error is small, affects mostly upper tropospheric retrievals (at 300 hPa: ∼ 2.6, 0.8, and 3.2 percent points for MOPITT thermal-infrared, near-infrared, and multispectral, respectively), and may have resulted in the overestimation of MOPITT retrieval biases in that region. TROPOMI can retrieve CO under both clear and cloudy conditions. The latter is achieved by quantifying interfering trace gases and parameters describing the cloud contamination of the measurements together with the CO column; then, the reference CO profiles used in the retrieval are scaled based on estimated above-cloud CO rather than on estimated total CO. We use AirCore measurements as the reference to evaluate the error introduced by this approach in cloudy TROPOMI retrievals over land after accounting for TROPOMI's vertical sensitivity to CO (relative bias and its standard deviation = 2.02 % ± 11.13 %). We also quantify the null-space error, which accounts for differences between the shape of TROPOMI reference profiles and that of AirCore measured profiles (for TROPOMI cloudy enull=0.98 % ± 2.32 %).

The field of clinical research has changed considerably in the past 20 years. As the work in this realm has come to embody far more than the pursuit of improved patient care, this has meant that staff supporting the research are asked to take on additional responsibilities, learn new processes, and be continuously educated on modernized policies and procedures. To address the increased responsibilities and complexities of work, Duke University School of Medicine leadership agreed that an overhaul of job descriptions for clinical research professionals was needed. A working group was created, assembling administrative leaders, human resources professionals, and clinical research subject matter experts. The Clinical Research Professionals Working Group (CRPWG) aimed to simplify the number of job classifications at Duke from approximately 80 to 12 and utilize a competency-based approach to professionalize the clinical research professionals working environment. The Joint Task Force for Clinical Trials Competency (JTFCTC) developed draft competencies that were used as the foundation to develop a tool that helped define job descriptions and map incumbent employees into the new jobs. Almost 600 employees were mapped using the competency-based tool. This paper describes the processes used to develop the competency-based tool and map incumbents, and provides the results and lessons learned of the mapping. A strong workforce of clinical research professionals will enable higher quality research and ultimately lead to better patient care and health outcomes.

In this article, we review the recent expansion within the sales education literature from five primary journals and the business literature at large. The five primary journals are the "Journal of Marketing Education, Marketing Education Review, Journal for the Advancement of Marketing Education, Journal of Education for Business," and the "Journal of Personal Selling & Sales Management." Of the 119 identified articles published in the past 15 years, experiential learning, career development, and assessment were the three most prominent topics. Of these, 69% were published after the Cummins et al. review article "A Critical Review of the Literature for Sales Educator," which was published in "Journal of Marketing Education" as a means of expanding sales education research. Future research opportunities in sales education are offered with suggested hypotheses for investigation. A reference table of classroom innovations is provided as an easy guide to instructors seeking vetted pedagogical enhancements. [For "A Critical Review of the Literature for Sales Educators," see EJ997395.]

Given the recent proliferation in sales programs, business colleges face a new set of challenges. Sales competencies are changing rapidly, and firms struggle with identifying and attracting sales candidates on campus. Therefore, it is important that we understand needed competencies and how the content of job advertisements may differentially appeal to various student populations. To do so, we develop a conceptual model, based on signaling theory, that focuses on how students formulate their intention to pursue a given sales position. Our research utilizes a two-study approach. First, we explore the desired entry-level sales skills communicated by employers through job advertisements. Next, we examine both student and advertisement characteristics and their distinct relationships with the satisfaction with the job ad and the intention to apply for a sales position. Our study is unique, as we examine distinct undergraduate groups' (sales, marketing, other business, and nonbusiness students) responses to sales job advertisements. Our findings demonstrate that differences in job ad clusters and student group characteristics influence the intention to pursue a sales position. Specifically, different student majors perceive job characteristics communicated within job ads differently. As such, our research provides insight into academic programs as well as corporate sales recruiters.

Characteristics of the Version 9 (V9) MOPITT (Measurements of Pollution in the Troposphere) satellite retrieval product for tropospheric carbon monoxide (CO) are described. The new V9 product includes many CO retrievals over land which, in previous MOPITT product versions, would have been discarded by the cloud detection algorithm. Globally, the number of daytime MOPITT retrievals over land has increased by 30 %–40 % relative to the Version 8 product, although the increase in retrieval coverage exhibits significant geographical variability. Areas benefiting from the improved cloud detection performance include (but are not limited to) source regions often characterized by high aerosol concentrations. The V9 MOPITT product also incorporates a modified calibration strategy for the MOPITT near-infrared (NIR) CO channels, resulting in greater temporal consistency for the NIR-only and thermal-infrared–near-infrared (TIR–NIR) retrieval variants. Validation results based on in situ CO profiles acquired from aircraft in a variety of contexts indicate that retrieval biases for V9 are typically within the range of ±5 % and are generally comparable to results for the V8 product.

Growing emissions from Pacific Northwest wildfires have increased atmospheric carbon monoxide in August, raising questions about potential health impacts as the seasonal pattern of air quality changes for large regions of North America.

The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument has been measuring global tropospheric carbon monoxide (CO) since March 2000, providing the longest nearly continuous record of CO from space. During its long mission, the data processing algorithms have been updated to improve the quality of CO retrievals and the sensitivity to the lower troposphere. Currently, MOPITT retrievals are only performed for clear-sky observations or over low clouds for ocean scenes. The cloud detection scheme was modified in the new V9 product, resulting in an improvement in observational coverage, especially over land. Comparison of the spatial and seasonal variations of the data coverage in V9 and V8 shows differences with significant geographical and temporal variability, with some regions such as Canada and the Amazon exhibiting a doubling of data in winter. Here we conducted an analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) cloud heights and cloud mask products along with MOPITT retrieval cloud flag descriptors to understand the impact of cloud conditions on the MOPITT observational coverage, with a particular focus on observations over Canada. The MOPITT CO total column (TC) data were modified by turning off the cloud detection scheme to allow for a CO retrieval result, regardless of their cloud status. Analyses of the standard V8 CO TC product (cloud filtered) and non-standard product (non-cloud-masked) were conducted for selected days. Results showed some coherent structures that were observed frequently in the non-masked CO product that was not present in the V8 product and could potentially be actual CO features. Many times, these CO plumes were also seen in the Infrared Atmospheric Sounding Interferometer (IASI) CO TC product. The MODIS cloud height analysis revealed that a significant number of low-cloud CO retrievals were discarded in the V8 product. Most of the missed CO plumes in the V8 product are now detected in the new V9 product as a result of the dependence of the MOPITT radiance ratio (MRT) test over land. Comparisons of the MRT and MODIS cloud height data indicate a remarkable negative correlation. As a result of the modified V9 cloud detection algorithm, a significant portion of the low-cloud CO retrievals is now incorporated in the new V9 MOPITT product. Consequently, the observational coverage over Canada is significantly improved, which benefits analyses of regional CO variability, especially during extreme pollution events. We also conducted a comparison of MOPITT and IASI CO TC and found generally good agreement, with about a 5 %–10 % positive bias that is more pronounced in highly polluted scenes.

ABSTRACT To ensure sufficient sensitivity and specificity of lateral flow tests for the detection of SARS-CoV-2 antigen, manufacturers recommend appropriate conditions for storage, including a temperature range. However, there is a high likelihood that kits will be exposed to temperatures outside of this range during transit to some regions. In this prospective study, we evaluated the sensitivity and specificity of the COVID-19 At-Home Test kits (manufactured by SD Biosensor/distributed by Roche) currently being delivered through a US Government program, after exposure to a range of hot and cold temperatures. COVID-19 At-Home Test kits were stored at up to 5 different temperatures: frozen (−4.0°F [−20.0°C]), refrigerated (42.8°F [6.0°C]), room temperature (68.0°F [20.0°C]), warm (98.0°F [36.7°C]), and excessive heat (118.0 to 126.0°F [47.8 to 52.2°C]) for 24 h and left at room temperature for 60, 90, or 120 min before use. Test kits were also stored for 48 h, 1 week, or 2 weeks in frozen, warm, and excessive heat conditions, and left for 60 or 120 min before use. In each scenario (storage temperature + time at room temperature), 5 positive and 5 negative control samples were applied, and line intensity was recorded using a color scale (0 to 100%). In every scenario, every positive sample resulted in strong signal intensity (≥26%), and every negative sample returned a negative result. This study suggests that exposure of up to 2 weeks to extreme temperatures, such as those that may occur in transit, does not impact the stability of the COVID-19 At-Home Test. IMPORTANCE COVID-19 At-Home Test kits may be exposed to extreme temperatures in transit, which may impact test sensitivity and specificity. We investigated assay ability to identify SARS-CoV-2 antigen after 24 h to 2 weeks in frozen, refrigerated, room temperature, warm, or excessive heat conditions. The assay correctly identified all positive and negative samples in all scenarios. This study suggests that exposure of up to 2 weeks to extreme temperatures, such as those that may occur in transit, does not impact the stability of the COVID-19 At-Home Test.

Expanded access (EA) provides a pathway for the clinical use of investigational products (drugs, biologics, and medical devices) for patients who are without satisfactory therapeutic options and for whom a clinical trial is not available. Academic medical centers (AMCs) are likely to encounter EA requests, but it is unknown what support is available at these institutions for physicians seeking EA for patients. Methods: A landscape assessment was conducted at AMCs, focused on those within the Clinical and Translational Science Awards (CTSA) consortium. Results: Forty-seven responses were evaluated including 42 CTSA hubs. The large majority (43 of 47 respondents) reported using single-patient EA, while 37 reported multi-patient industry sponsored EA and 37 reported multi-patient investigator-initiated EA. Only half reported central tracking of EA requests. Support was available at 89% of sites for single-patient EA but less often for multi-patient EA. Types of support varied and were focused largely on the initial submission to the FDA. Conclusion: Use of and support for EA is widespread at AMCs, with support focused on single-patient requests. Gaps in support are common for activities after initial submission, such as FDA reporting and data collection.

Quality clinical research is essential for health care progress and is the mission of academic health centers. Yet ensuring quality depends on an institution’s ability to measure, control, and respond to metrics of trial performance. Uninformed clinical research provides little benefit to health care, drains institutional resources, and may waste participants' time and commitment. Opportunities for ensuring high-quality research are multifactorial, including training, evaluation, and retention of research workforces; operational efficiencies; and standardizing policies and procedures. Duke University School of Medicine has committed to improving the quality and informativeness of our clinical research enterprise through investments in infrastructure with significant focus on optimizing research management system integration as a foundational element for quality management. To address prior technology limitations, Duke has optimized Advarra’s OnCore for this purpose by seamlessly integrating with the IRB system, electronic health record, and general ledger. Our goal was to create a standardized clinical research experience to manage research from inception to closeout. Key drivers of implementation include transparency of research process data and generating metrics aligned with institutional goals. Since implementation, Duke has leveraged OnCore data to measure, track, and report metrics resulting in improvements in clinical research conduct and quality.

In 2016, Duke reconfigured its clinical research job descriptions and workforce to be competency-based, modeled around the Joint Taskforce for Clinical Trial Competency framework. To ensure consistency in job classification amongst new hires in the clinical research workforce, Duke subsequently implemented a Title Picker tool. The tool compares the research unit’s description of job responsibility needs against those standardized job descriptions used to map incumbents in 2016. Duke worked with human resources and evaluated the impact on their process as well as on the broader community of staff who hire clinical research professionals. Implementation of the tool has enabled Duke to create consistent job classifications for its workforce and better understand who composes the clinical research professional workforce. This tool has provided valuable workforce metrics, such as attrition, hiring, etc., and strengthened our collaboration with Human Resources.

© 2017 Author(s). Long-term measurements from satellites and surface stations have demonstrated a decreasing trend of tropospheric carbon monoxide (CO) in the Northern Hemisphere over the past decade. Likely explanations for this decrease include changes in anthropogenic, fires, and/or biogenic emissions or changes in the primary chemical sink hydroxyl radical (OH). Using remotely sensed CO measurements from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument, in situ methyl chloroform (MCF) measurements from the World Data Centre for Greenhouse Gases (WDCGG) and the adjoint of the GEOS-Chem model, we estimate the change in global CO emissions from 2001 to 2015. We show that the loss rate of MCF varied by 0.2% in the past 15 years, indicating that changes in global OH distributions do not explain the recent decrease in CO. Our two-step inversion approach for estimating CO emissions is intended to mitigate the effect of bias errors in the MOPITT data as well as model errors in transport and chemistry, which are the primary factors contributing to the uncertainties when quantifying CO emissions using these remotely sensed data. Our results confirm that the decreasing trend of tropospheric CO in the Northern Hemisphere is due to decreasing CO emissions from anthropogenic and biomass burning sources. In particular, we find decreasing CO emissions from the United States and China in the past 15 years, and unchanged anthropogenic CO emissions from Europe since 2008. We find decreasing trends of biomass burning CO emissions from boreal North America, boreal Asia and South America, but little change over Africa. In contrast to prior results, we find that a positive trend in CO emissions is likely for India and southeast Asia.

Sub-grid variability (SGV) in atmospheric trace gases within satellite pixels is a key issue in satellite design and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m×250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by the mean of the sub-grid GeoTASO values within the same satellite pixel) for different hypothetical satellite pixel sizes over urban regions. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS-AQ field campaign and over the Los Angeles Basin, USA, during the 2017 Student Airborne Research Program (SARP) field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ∼10 % for 0.5 km×0.5 km pixel size to ∼35 % for 25 km×25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability in the retrieved NO2 VC within the same hypothetical satellite pixels (represented by the difference of retrieved values at two or more different times in a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over the SMA. For a given small (e.g., ≤4 h) sampling time difference within the same satellite pixels, the temporal variability in the retrieved NO2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles Basin. The results of this study have implications for future satellite design and retrieval interpretation and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime.

The response from many firms to the recent COVID-19 crisis underscores a more fundamental and overarching question: How should salespeople and their firms prepare for and respond to sales crises more generally? In response, a group of sales scholars recently convened at the American Marketing Association's Winter Conference to discuss the challenges and opportunities facing salespeople and their firms before, during, and after a sales crisis. Thus, based on this session and subsequent follow-up discussions, this paper develops a framework detailing how the sales function should prepare for, withstand, and learn from a sales crisis. In so doing, we argue that sales crises can originate internally and externally to an organization and can impact the entire sales organization, the sales manager, and/or individual salespeople. Moreover, viewing a sales crisis simply as a turning point rather than a devastating event, we highlight the implications for salespeople and their managers who must inevitably deal with sales crises and the changes involved. Finally, we conclude with potential future directions for sales scholars interested in exploring the impact of crises on the sales function, as the next sales crisis is likely just around the corner. [ABSTRACT FROM AUTHOR]

We have analyzed TROPOspheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data acquired between November 2017 and March 2019 with respect to other satellite (MOPITT, Measurement Of Pollution In The Troposphere) and airborne (ATom, Atmospheric Tomography mission) datasets to better understand TROPOMI's contribution to the global tropospheric CO record (2000 to present). MOPITT and TROPOMI are two of only a few satellite instruments to ever derive CO from solar-reflected radiances. Therefore, it is particularly important to understand how these two datasets compare. Our results indicate that TROPOMI CO retrievals over land show excellent agreement with respect to MOPITT: relative biases and their SD (i.e., accuracy and precision) are on average -3.73%±11.51%, -2.24%±12.38%, and -3.22%±11.13% compared to the MOPITT TIR (thermal infrared), NIR (near infrared), and TIR + NIR (multispectral) products, respectively. TROPOMI and MOPITT data also show good agreement in terms of temporal and spatial patterns. Despite depending on solar-reflected radiances for its measurements, TROPOMI can also retrieve CO over bodies of water if clouds are present by approximating partial columns under cloud tops using scaled, model-based reference CO profiles. We quantify the bias of TROPOMI total column retrievals over bodies of water with respect to colocated in situ ATom CO profiles after smoothing the latter with the TROPOMI column averaging kernels (AKs), which account for signal attenuation under clouds (relative bias and its SD =3.25%±11.46 %). In addition, we quantify enull (the null-space error), which accounts for differences between the shape of the TROPOMI reference profile and that of the ATom true profile (enull=2.16%±2.23 %). For comparisons of TROPOMI and MOPITT retrievals over open water we compare TROPOMI total CO columns to their colocated MOPITT TIR counterparts. Relative bias and its SD are 2.98 %±15.71 % on average. We investigate the impact of discrepancies between the a priori and reference CO profiles (used by MOPITT and TROPOMI, respectively) on CO retrieval biases by applying a null-space adjustment (based on the MOPITT a priori) to the TROPOMI total column values. The effect of this adjustment on MOPITT and TROPOMI biases is minor, typically 1–2 percentage points.

AbstractIntroduction:A new competency-based job framework was implemented for clinical research professionals at a large, clinical research-intensive academic medical center. This study evaluates the rates of turnover before and after implementation of the new framework. Turnover in this workforce (as with most) is costly; it contributes to wasted dollars and lost productivity since these are highly specialized positions requiring extensive training, regardless of experience in the field.Methods:Trends in employee turnover for 3 years prior to and after the implementation of competency-based job framework for clinical research positions were studied using human resources data. Employee demographics, turnover rates, and comparisons to national statistics are summarized.Results:Employee turnover within the clinical research professional jobs has decreased from 23% to 16%, a 45% reduction, since the implementation of competency-based job framework.Conclusion:The new jobs and career ladders, both of which are centered on a competency-based framework, have decreased the overall turnover rate in this employee population. Since little is known about the rates of turnover in clinical research, especially in the academic medical setting, the results of this analysis can provide important insights to other academic medical centers on both employee turnover rate in general and the potential impact of implementing large-scale competency-based job changes.

The Measurements of Pollution in the Troposphere (MOPITT) retrievals over urban regions have not been validated systematically, even though MOPITT observations are widely used to study CO over urban regions. Here we compare MOPITT products over urban and non-urban regions with aircraft measurements from the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ – 2011–2014), Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS – 2013), Air Chemistry Research In Asia (ARIAs – 2016), A-FORCE (2009, 2013), and Korea United States Air Quality (KORUS-AQ – 2016) campaigns. In general, MOPITT agrees reasonably well with the in situ profiles, over both urban and non-urban regions. Version 8 multispectral product (V8J) biases vary from −0.7 % to 0.0 % and version 8 thermal-infrared product (TIR) biases vary from 2.0 % to 3.5 %. The evaluation statistics of MOPITT V8J and V8T over non-urban regions are better than those over urban regions with smaller biases and higher correlation coefficients. We find that the agreement of MOPITT V8J and V8T with aircraft measurements at high CO concentrations is not as good as that at low CO concentrations, although CO variability may tend to exaggerate retrieval biases in heavily polluted scenes. We test the sensitivities of the agreements between MOPITT and in situ profiles to assumptions and data filters applied during the comparisons of MOPITT retrievals and in situ profiles. The results at the surface layer are insensitive to the model-based profile extension (required due to aircraft altitude limitations), whereas the results at levels with limited aircraft observations (e.g., the 600 hPa layer) are more sensitive to the model-based profile extension. The results are insensitive to the maximum allowed time difference criterion for co-location (12, 6, 3, and 1 h) and are generally insensitive to the radius for co-location, except for the case where the radius is small (25 km), and hence few MOPITT retrievals are included in the comparison. Daytime MOPITT products have smaller overall biases than nighttime MOPITT products when comparing both MOPITT daytime and nighttime retrievals to the daytime aircraft observations. However, it would be premature to draw conclusions on the performance of MOPITT nighttime retrievals without nighttime aircraft observations. Applying signal-to-noise ratio (SNR) filters does not necessarily improve the overall agreement between MOPITT retrievals and in situ profiles, likely due to the reduced number of MOPITT retrievals for comparison. Comparisons of MOPITT retrievals and in situ profiles over complex urban or polluted regimes are inherently challenging due to spatial and temporal variabilities of CO within MOPITT retrieval pixels (i.e., footprints). We demonstrate that some of the errors are due to CO representativeness with these sensitivity tests, but further quantification of representativeness errors due to CO variability within the MOPITT footprint will require future work.

Abstract Background: Adequate equitable recruitment of underrepresented groups in clinical research and trials is a national problem and remains a daunting challenge to translating research discoveries into effective healthcare practices. Engagement, recruitment, and retention (ER&R) training programs for Clinical Research Professionals (CRPs) often focus on policies and regulations. Although some training on the importance of diversity and inclusion in clinical research participation has recently been developed, there remains a need for training that couples critical equity, diversity, and inclusion (EDI) concepts with skill development in effective recruitment and retention strategies, regulations, and best practices. Approach and methods: We developed the ER&R Certificate program as a holistic approach to provide Duke University CRPs the opportunity to build competency in gap areas and to increase comfort in championing equitable partnerships with clinical research participants. The thirteen core and elective courses include blended learning elements, such as e-learning and wiki journaling prompts, to facilitate meaningful discussions. Pre- and post-assessments administered to CRP program participants and their managers assessed program impact on CRP skills in ER&R tasks and comfort in equitable, diverse, and inclusive engagement of clinical research participants. Results and discussion: Results from the first two cohorts indicate that CRPs perceived growth in their own comfort with program learning objectives, especially those centered on participant partnership and EDI principles, and most managers witnessed growth in competence and responsibility for ER&R-related tasks. Results suggest value in offering CRPs robust training programs that integrate EDI and ER&R training.