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Single-arm studies, particularly single-arm trials (SATs), are increasingly being used in submissions for marketing authorization and health technology assessment. As reviewers of evidence, we sought to better understand the validity of SATs, compared with observational single-arm studies (case series), and how to assess them in our reviews.
Methods
We conducted a highly pragmatic literature review to create a convenience sample of recent systematic reviews published from January to July 2023 to establish the following: (i) what single-arm study designs are included; (ii) what quality assessment tools are used; and (iii) whether there is a difference in effect size and variability among different study designs. A single reviewer identified reviews of interventions that included single-arm studies and extracted information on the numbers of included SATs and case series, and the quality assessment tools used. Any misclassifications by review authors were identified. For meta-analyses, outcome data were extracted and a subgroup analysis comparing SATs and case series was conducted.
Results
Work is still underway to complete this investigation. So far, it appears that a large proportion of systematic reviews misclassify SATs and case series studies and few use appropriate quality assessment tools. There is not yet any evidence of a systematic difference between SATs and case series in terms of effect size.
Conclusions
Findings suggest that there is poor understanding of SATs in the review community. There are limited specific quality assessment tools for SATs and review authors frequently use inappropriate tools to assess them. More research is likely to be needed to investigate the relative validity of SATs and single-arm observational studies.
As the most internally rigorous design, randomized controlled trials (RCTs) are the gold standard for assessing the efficacy and safety profile of interventions. Increasingly, health technology assessment (HTA) considers evidence from non-randomized studies. Guidance recommends synthesizing different study designs separately due to their different inherent biases/limitations. But when authors or reviewers misclassify studies, this could affect which studies are included and therefore have an impact on review results.
Methods
We are conducting a methods project to (i) identify a clear study design classification system, (ii) explore whether its use produces consistent study design categorizations among reviewers, and (iii) iteratively improve the classification system. We performed a pragmatic web-based search for study design categorization tools and used the resulting schemas to develop a clear algorithm for use by reviewers of all levels of experience, specifically in reviews of treatment interventions. Next, we tested tool consistency and user experience by web-based survey in a small internal sample of reviewers, each independently using the system to categorize 18 published studies.
Results
A median of seven reviewers (range four to eight) categorized each study. Rater agreement using the chart varied widely, with 100 percent agreement on the designs of three studies (17%), and at least 75 percent of reviewers agreeing on one design for nine studies (50%). The most common agreement was reached on RCTs and non-randomized controlled trials. The most common sources of disagreement were between different types of cohort studies and between case series and controlled cohort studies, largely due to inconsistent reporting. We also identified several improvements: the wording of prompt questions, the ordering of designs, and the addition of new elements.
Conclusions
The classification system as initially designed led to too much variation in study design categorization to be useful. Consequently, we present a revised version that we now aim to evaluate in a larger sample of reviewers. Further research will also investigate whether using the tool would change the results of systematic reviews, using a small sample of published reviews.
While systematic reviews (SRs) are regarded as the gold standard in healthcare evidence reviewing (and a requirement of many health technology assessments [(HTAs)]), other types of review also play an important role throughout a product’s lifecycle. Drawing on more than thirty years’ experience in conducting reviews, we present key points to consider when deciding which review type might be required.
Methods
SRs are recommended when a comprehensive search and synthesis approach is required, for example HTAs. They have highly structured methods, emphasizing bias minimization, transparency, and replicability. “Rapid,” “pragmatic,” or “targeted” reviews are increasingly popular due to their accelerated timelines and reduced costs, with methodological shortcuts possible at various stages. Scoping reviews explore what is known about a topic and typically have a broad research question. “Reviews of reviews” or “overviews” identify existing SRs on an established topic. Finally, “living reviews” follow the same process as an SR or rapid review but incorporate new evidence on a continual or regular basis.
Results
Rapid reviews may be appropriate when flexibility exists regarding the scope and review methods. Any limitations due to methodological shortcuts must be acknowledged in a transparent manner. Scoping reviews are useful for pioneering research ahead of an SR, or early in a product’s development phase, when an overall understanding of the evidence base is required. Reviews of reviews are particularly useful when the size of the primary study literature means that a review of primary studies would be unfeasible. Living reviews are best suited to topics where the evidence base is changing rapidly, or the best information is needed quickly.
Conclusions
When considering conducting or commissioning a review, organizations should consider the intended audience for the review, the resources, time, and budget available, and the size of the existing literature. Although SRs remain the gold standard, a rapid review, scoping review, or review of reviews may offer a more suitable way to approach a given research question.
Conducting a systematic review (SR) of clinical trials is labor-intensive and expensive. However, existing open-source content can be used to develop custom machine learning tools suited to the workflow of individual organizations. This case study details the potential of a bespoke tool developed by York Health Economics Consortium (YHEC) for reducing the time and cost involved in producing an SR.
Methods
RESbot is a flexible, stand-alone machine learning tool created using an extensively tested open-source dataset developed by Cochrane. The tool identifies randomized controlled trials (RCTs) from a large corpus of records. It has a user interface and inputs/outputs to fit into the company’s existing workflow at any stage. RESbot has two settings. The “sensitive” setting identifies a higher number of possible RCTs with a lower risk of missing eligible studies, while the “precise” setting is more focused. For both settings, we estimated the reduction in resources required for record screening in two examples of RCT-only reviews.
Results
Scoping searches in MEDLINE were conducted for SRs of RCTs in femoropopliteal artery diseases (FAD) and postpartum depression (PD). The results were run through RESbot. For the FAD SR, 1,444 references were retrieved, with the sensitive and precise RESbot settings reducing the record set by 38 percent and 64 percent, respectively. For the PD SR, a record set of 2,153 records was reduced by 25 percent and 41 percent, respectively. Resource savings offered by RESbot vary depending on subject but may reduce the time taken to screen records by up to 64 percent, with a subsequent reduction in cost to the organization commissioning the SR.
Conclusions
The use of bespoke machine learning tools in SR production has the potential to reduce the time and staff costs involved in producing a review. This case study tested the effect on a small number of records, but for larger reviews retrieving tens of thousands of records, reductions in time and costs can be very significant.
There is no consensus on how to determine appropriate financial compensation for research recruitment. Selecting incentive amounts that are reasonable and respectful, without undue inducement, remains challenging. Previously, we demonstrated that incentive amount significantly impacts participants’ willingness to complete various hypothetical research activities. Here we further explore this relationship in a mock decentralized study.
Methods:
Adult ResearchMatch volunteers were invited to join a prospective study where interested individuals were given an opportunity to view details for a study along with participation requirements, then offered a randomly generated compensation amount between $0 and $50 to enroll and participate. Individuals agreeing to participate were then asked to complete tasks using a remote mobile application (MyCap), for two weeks. Tasks included a weekly survey, a daily gratitude journal and daily phone tapping task.
Results:
Willingness to participate was 85% across all incentive levels but not significantly impacted by amount. Task completion appeared to increase as a function of compensation until a plateau at $25. While participants described the study as low burden and reported that compensation was moderately important to their decision to join, only 31% completed all study tasks.
Conclusion:
While offering compensation in this study did not have a strong effect on enrollment rate, this work provides insight into participant motivation when joining and participating in studies employing mobile applications.
OBJECTIVES/GOALS: Measuring the area of skin involvement in chronic graft-versus-host disease (cGVHD) relies on costly, time-consuming manual assessment, with high disagreement among experts (>20%). Our published AI method, trained on labeled 3D photos, showed promise for delineating affected areas. We aim to improve its performance using unlabeled 2D photos. METHODS/STUDY POPULATION: Our published AI model (baseline) was trained on 360 labeled photos of 36 cGVHD patients,from a 3D camera with calibrated distance and lighting.Our gold standard labels were contours around affected skin, marked by a trained expert. A second unlabeledcohort of 974 standard 2D photos of 8 cGVHD patients was used to improve the baseline model. First the baseline model predicted affected areas on the unlabeled photos. Photos with good predictions were added to the training set with their AI-predicted labels. The model was then re-trained with the expanded labeled set. Models were successively trained with more AI labels until performance stopped improving. AI performance was assessed on a test set of 20 photos from 20 patients unseen during training, labeled by 4 experts to improve accuracy. RESULTS/ANTICIPATED RESULTS: Model performance was calculated by comparing against the gold standard labels on the test set. To quantify the spatial overlap of labeled areas the Dice coefficient was used (0 is no overlap, 1 is complete agreement), where higher values are better. To estimate clinical error we used surface area error (Error), where lower values are better. On the test set, the baseline model had a median Dice of 0.57 [interquartile range: 0.39 – 0.82] and Error of 57.6% [20.2 – 103.3%]. Re-training with additional AI-predicted labels from 8 new patients, the model yielded a median Dice of 0.60 [0.35 – 0.80] and Error of 50% [12.5 – 103.8%]. This approach is being expanded to a further 300 unlabeled patients, where we anticipate significant improvements to AI performance and consistency. DISCUSSION/SIGNIFICANCE: Evaluating AI models in standard photos could provide a consistent method of assessing and tracking cutaneous cGVHD and relieve the burden of costly expert assessment. A reliable automated AI tool would provide a meaningful improvement to the current standard of manual assessment and could be easily applied to large patient cohorts.
Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.
Participants and Methods:
Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.
Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.
Results:
We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).
Conclusions:
This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.
Systematic reviews are important for informing decision-making and primary research, but they can be time consuming and costly. With the advent of machine learning, there is an opportunity to accelerate the review process in study screening. We aimed to understand the literature to make decisions about the use of machine learning for screening in our review workflow.
Methods
A pragmatic literature review of PubMed to obtain studies evaluating the accuracy of publicly available machine learning screening tools. A single reviewer used ‘snowballing’ searches to identify studies reporting accuracy data and extracted the sensitivity (ability to correctly identify included studies for a review) and specificity, or workload saved (ability to correctly exclude irrelevant studies).
Results
Ten tools (AbstractR, ASReview Lab, Cochrane RCT classifier, Concept encoder, Dpedia, DistillerAI, Rayyan, Research Screener, Robot Analyst, SWIFT-active screener) were evaluated in a total of 16 studies. Fourteen studies were single arm where, although compared with a reference standard (predominantly single reviewer screening), there was no other comparator. Two studies were comparative, where tools were compared with other tools as well as a reference standard. All tools ranked records by probability of inclusion and either (i) applied a cut-point to exclude records or (ii) were used to rank and re-rank records during screening iterations, with screening continuing until most relevant records were obtained. The accuracy of tools varied widely between different studies and review projects. When used in method (ii), at 95 percent to 100 percent sensitivity, tools achieved workload savings of between 7 percent and 99 percent. It was unclear whether evaluations were conducted independent of tool developers.
Conclusions
Evaluations suggest the potential for tools to correctly classify studies in screening. However, conclusions are limited since (i) tool accuracy is generally not compared with dual reviewer screening and (ii) the literature lacks comparative studies and, because of between-study heterogeneity, it is not possible to robustly determine the accuracy of tools compared with each other. Independent evaluations are needed.
Improving the quality and conduct of multi-center clinical trials is essential to the generation of generalizable knowledge about the safety and efficacy of healthcare treatments. Despite significant effort and expense, many clinical trials are unsuccessful. The National Center for Advancing Translational Science launched the Trial Innovation Network to address critical roadblocks in multi-center trials by leveraging existing infrastructure and developing operational innovations. We provide an overview of the roadblocks that led to opportunities for operational innovation, our work to develop, define, and map innovations across the network, and how we implemented and disseminated mature innovations.
Since the initial publication of A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals in 2008, the prevention of healthcare-associated infections (HAIs) has continued to be a national priority. Progress in healthcare epidemiology, infection prevention, antimicrobial stewardship, and implementation science research has led to improvements in our understanding of effective strategies for HAI prevention. Despite these advances, HAIs continue to affect ∼1 of every 31 hospitalized patients,1 leading to substantial morbidity, mortality, and excess healthcare expenditures,1 and persistent gaps remain between what is recommended and what is practiced.
The widespread impact of the coronavirus disease 2019 (COVID-19) pandemic on HAI outcomes2 in acute-care hospitals has further highlighted the essential role of infection prevention programs and the critical importance of prioritizing efforts that can be sustained even in the face of resource requirements from COVID-19 and future infectious diseases crises.3
The Compendium: 2022 Updates document provides acute-care hospitals with up-to-date, practical expert guidance to assist in prioritizing and implementing HAI prevention efforts. It is the product of a highly collaborative effort led by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Disease Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of organizations and societies with content expertise, including the Centers for Disease Control and Prevention (CDC), the Pediatric Infectious Disease Society (PIDS), the Society for Critical Care Medicine (SCCM), the Society for Hospital Medicine (SHM), the Surgical Infection Society (SIS), and others.
Adverse effects are a common concern when prescribing and reviewing medication, particularly in vulnerable adults such as older people and those with intellectual disability. This paper describes the development of an app giving information on side-effects, called Medichec, and provides a description of the processes involved in its development and how drugs were rated for each side-effect. Medications with central anticholinergic action, dizziness, drowsiness, hyponatraemia, QTc prolongation, bleeding and constipation were identified using the British National Formulary (BNF) and frequency of occurrence of these effects was determined using the BNF, product information and electronic searches, including PubMed.
Results
Medications were rated using a traffic light system according to how commonly the adverse effect was known to occur or the severity of the effect.
Clinical implications
Medichec can facilitate access to side-effects information for multiple medications, aid clinical decision-making, optimise treatment and improve patient safety in vulnerable adults.
Clinical trials face many challenges with meeting projected enrollment and retention goals. A study’s recruitment materials and messaging convey necessary key information and therefore serve as a critical first impression with potential participants. Yet study teams often lack the resources and skills needed to develop engaging, culturally tailored, and professional-looking recruitment materials. To address this gap, the Recruitment Innovation Center recently developed a Recruitment & Retention Materials Content and Design Toolkit, which offers research teams guidance, actionable tips, resources, and customizable templates for creating trial-specific study materials. This paper seeks to describe the creation and contents of this new toolkit.
The DIMA Network (Developing Innovative Multi-proxy Analyses – in Siberia and the Russian Far East (SRFE)) started from a small nucleus of palaeoenvironmental researchers in the UK and SRFE at a workshop in 2008 and currently includes researchers from over 25 institutions. The mutual interest in creating long-term records of environmental change was rekindled during workshops in Magadan (2018), Tomsk (2018) and Southampton (2019). These events were organised to connect researchers from the UK and SRFE with these aims: (1) provide training in new techniques and methods, (2) facilitate knowledge transfer about local sites and conditions, (3) stimulate large-scale collaborative projects across SRFE and (4) inspire a new generation of palaeoenvironmental researchers.
The antipsychotic aripiprazole is often used in the treatment of first-episode psychosis. Measuring aripiprazole blood levels provides an objective measure of treatment adherence, but this currently involves taking a venous blood sample and sending to a laboratory for analysis.
Aims
To detail the development, validation and utility of a new point of care (POC) test for finger-stick capillary blood concentrations of aripiprazole.
Method
Analytical performance (sensitivity, precision, recovery and linearity) of the assay were established using spiked whole blood and control samples of varying aripiprazole concentration. Assay validation was performed over a 14-month period starting in July 2021. Eligible patients were asked to provide a finger-stick capillary sample in addition to their usual venous blood sample. Capillary blood samples were tested by the MyCare™ Insite POC analyser, which provided measurement of aripiprazole concentration in 6 min, and the venous blood sample was tested by the standard laboratory method.
Results
A total of 101 patients agreed to measurements by the two methods. Venous blood aripiprazole concentrations as assessed by the laboratory method ranged from 17 to 909 ng/mL, and from 1 to 791 ng/mL using POC testing. The correlation coefficient between the two methods (r) was 0.96 and there was minimal bias (slope 0.91, intercept 4 ng/ml).
Conclusions
The MyCare Insite POC analyser is sufficiently accurate and reliable for clinical use. The availability of this technology will improve the assessment of adherence to aripiprazole and the optimising of aripiprazole dosing.
Edited by
Ziwei Qi, Fort Hays State University, Kansas,April N. Terry, Fort Hays State University, Kansas,Tamara J. Lynn, Fort Hays State University, Kansas
As a research team tasked with developing local initiatives for juvenile justice practices, our entry into rural research sites was built on listening tours and semi-structured gatherings involving more than 100 community stakeholders. At every point, we were surprised to see almost no reference to gender or the unique experiences of systeminvolved girls. (Sue, personal narrative, 15 March 2020)
The noteworthy long silences about gender encountered during fieldwork in rural communities stand in sharp contrast to the ubiquity of genderbased inequities around the globe. Gender-based violence (GBV) is a deeply entrenched issue of gender inequality and discrimination (Council of Europe, 2011), including emotional and psychological harm (Ott, 2017), deprivation of liberty (United Nations General Assembly, 1993) and human rights violations (European Institute for Gender Equality, 2020). According to a study conducted by the World Health Organization (WHO), one in three women who have been in a relationship have experienced physical and/or sexual violence by an intimate partner or sexual violence from a non-partner at some point in their lives (WHO, 2021).
The paucity of attention to GBV for at-risk girls and subsequent involvement within the juvenile justice system is even more stark. First, age matters. While intimate partner violence is the most prevalent form of violence against women, younger women remain at highest risk (WHO, 2021). Leading studies (such as WHO, 2021) document the damaging consequences of violence on women's health, including risks for injuries, depression, anxiety disorders, sexually transmitted infections and other health problems. Little attention is directed towards ‘hidden harms’ for at-risk girls, and especially those in rural areas, and the existing few have focused exclusively on urban locations, ignoring the unique sociopolitical differences of rural communities. The current study directs attention towards this continued oversight, demanding intentional efforts towards gender-responsive needs, including indirect forms of GBV, for at-risk girls everywhere, including rural areas.
These authors conducted a two-year research project on juvenile justice issues in isolated areas of western Kansas within the United States. As background, in 2016, the state of Kansas passed a comprehensive juvenile justice reform bill, Senate Bill 367 (SB 367) (Kansas Legislative Session, 2017).
One challenge for multisite clinical trials is ensuring that the conditions of an informative trial are incorporated into all aspects of trial planning and execution. The multicenter model can provide the potential for a more informative environment, but it can also place a trial at risk of becoming uninformative due to lack of rigor, quality control, or effective recruitment, resulting in premature discontinuation and/or non-publication. Key factors that support informativeness are having the right team and resources during study planning and implementation and adequate funding to support performance activities. This communication draws on the experience of the National Center for Advancing Translational Science (NCATS) Trial Innovation Network (TIN) to develop approaches for enhancing the informativeness of clinical trials. We distilled this information into three principles: (1) assemble a diverse team, (2) leverage existing processes and systems, and (3) carefully consider budgets and contracts. The TIN, comprised of NCATS, three Trial Innovation Centers, a Recruitment Innovation Center, and 60+ CTSA Program hubs, provides resources to investigators who are proposing multicenter collaborations. In addition to sharing principles that support the informativeness of clinical trials, we highlight TIN-developed resources relevant for multicenter trial initiation and conduct.
To evaluate the impact of a diagnostic stewardship intervention on Clostridioides difficile healthcare-associated infections (HAI).
Design:
Quality improvement study.
Setting:
Two urban acute care hospitals.
Interventions:
All inpatient stool testing for C. difficile required review and approval prior to specimen processing in the laboratory. An infection preventionist reviewed all orders daily through chart review and conversations with nursing; orders meeting clinical criteria for testing were approved, orders not meeting clinical criteria were discussed with the ordering provider. The proportion of completed tests meeting clinical criteria for testing and the primary outcome of C. difficile HAI were compared before and after the intervention.
Results:
The frequency of completed C. difficile orders not meeting criteria was lower [146 (7.5%) of 1,958] in the intervention period (January 10, 2022–October 14, 2022) than in the sampled 3-month preintervention period [26 (21.0%) of 124; P < .001]. C. difficile HAI rates were 8.80 per 10,000 patient days prior to the intervention (March 1, 2021–January 9, 2022) and 7.69 per 10,000 patient days during the intervention period (incidence rate ratio, 0.87; 95% confidence interval, 0.73–1.05; P = .13).
Conclusions:
A stringent order-approval process reduced clinically nonindicated testing for C. difficile but did not significantly decrease HAIs.
To examine the perspectives of caregivers that are not part of the antibiotic stewardship program (ASP) leadership team (eg, physicians, nurses, and clinical pharmacists), but who interact with ASPs in their role as frontline healthcare workers.
Design:
Qualitative semistructured interviews.
Setting:
The study was conducted in 2 large national healthcare systems including 7 hospitals in the Veterans’ Health Administration and 4 hospitals in Intermountain Healthcare.
Participants:
We interviewed 157 participants. The current analysis includes 123 nonsteward clinicians: 47 physicians, 26 pharmacists, 29 nurses, and 21 hospital leaders.
Methods:
Interviewers utilized a semistructured interview guide based on the Consolidated Framework for Implementation Research (CFIR), which was tailored to the participant’s role in the hospital as it related to ASPs. Qualitative analysis was conducted using a codebook based on the CFIR.
Results:
We identified 4 primary perspectives regarding ASPs. (1) Non-ASP pharmacists considered antibiotic stewardship activities to be a high priority despite the added burden to work duties: (2) Nurses acknowledged limited understanding of ASP activities or involvement with these programs; (3) Physicians criticized ASPs for their restrictions on clinical autonomy and questioned the ability of antibiotic stewards to make recommendations without the full clinical picture; And (4) hospital leaders expressed support for ASPs and recognized the unique challenges faced by non-ASP clinical staff.
Conclusion:
Further understanding these differing perspectives of ASP implementation will inform possible ways to improve ASP implementation across clinical roles.
The Recruitment Innovation Center (RIC) has created a toolkit of novel strategies to engage potential participants in response to recruitment and retention challenges associated with COVID-19 studies. The toolkit contains pragmatic, generalizable resources to help research teams increase awareness of clinical trials and opportunities to participate; produce culturally sensitive and engaging recruitment materials; improve consent and return of results processes; and enhance recruitment of individuals from populations disproportionately impacted by COVID-19. This resource, the “RIC COVID-19 Recruitment and Retention Toolkit,” is available free online. We describe the toolkit and the community feedback used to author and curate this resource.