Editorial: Building the clinical research workforce: challenges, capacities and competencies
Bibliographic record
Abstract
In this editorial, we summarize identified headwinds evident in the clinical research professional workforce, spanning capacity constraints and aligning competencies to meet the complexity of modern clinical research. This editorial is part of the research topic: "Building the Clinical Research Workforce: Challenges, Capacities and Competencies". To progress beyond common challenges, we outline opportunities for innovation in medicinal and pharmacological advancement from the collection.In the past decade and more considerably in the past five years, there has been heightened attention to the available resources and training within the clinical research workforce. With pharmaceutical research sponsors spending 50% more on average in research and development since 2018, and much of this spending and investment in novel therapies coming from emerging biopharma companies, the criticality of a workforce pipeline cannot be overstated during periods of intensive growth and market fluctuations in new drug and device development (Mullard, 2024).The foundation for core clinical research workforce competencies was established in 2014 with the initial publication of the harmonized Joint Task Force Clinical Trial Competency Framework (JTF Framework) as a means of establishing a common lexicon of critical functional abilities of personnel in order to adapt to innovative trial designs, complex trial conduct, and novel technologies (Sonstein et al., 2014). By 2024, the framework, with translations in 11 languages, had been applied both in the United States and internationally to educate, train, and support the clinical research workforce (Joint Task Force for Clinical Trial Competency, 2017;Sonstein et al., 2024). In the post-COVID-19 era, the aftershocks of increasing staff turnover rates and overall workforce contraction necessitated a harmonized response across a broad spectrum of employers: academic medical center research sites, cooperative groups, contract research organizations, and pharmaceutical companies, among others (Freel et al., 2023). The archetype of the clinical research professional (CRP) has become broadened to include all individuals who support the operationalization of clinical research, including not only clinical research coordinators, clinical research nurses and midwives, but also advanced practice providers, pharmaceutical industry research physicians (e.g., medical monitors), regulatory affairs professionals, data management professionals, grant and contract administrators, ethics committee members, clinical laboratory personnel and managers, quality assurance monitors and assistants (Mendell et al., 2024). This broad professional group continues to evolve but competency standards are necessary to meet the needs of a dynamic and constantly changing clinical research enterprise.Besides increasing staff turnover rates, additional challenges and gaps exist that affect institutions, researchers, and the CRP workforce. One gap is a generalized lack of public understanding of clinical research, which contributes to the lack of awareness that clinical research offers a career track for future employees. Most enter the profession "by accident" rather than having an intentional plan to enter the clinical research workforce at the end of secondary school (Freel et al., 2023) or higher education. As the general retirement cliff approaches for the current CRP workforce, attention is appropriately shifting to cultivating interest and inquiry among the next generation of research-engaged graduates. This includes the opportunity to recruit and retain CRPs from diverse backgrounds and communities which in turn may facilitate a higher degree of relatability among members of the public to feel welcome to participate in research.As part of an initiative to increase higher education integration of careers in clinical research, a competency-based curricula for training certificates, academic degrees, internships, and apprenticeships have been introduced to encourage earlier intentional entry to the field (Knapke et al., 2023). Kayla et al (2023) standardized job titles, descriptions, and career progression has resulted in promising enhancements in the professionalism of these roles through better-defined upward mobility and professional development pathways and significantly reduced turnover (Snyder et al., 2024).The confluence of new talent pools and paradigmatic shifts in trial design has resulted in a refreshed JTF Framework that includes new emerging competencies to support its 8-domain structure, including project management competencies (Sonstein et al., 2022). Keim-Malpass, Phillips, and Johnson (2023) propose a curriculum model that focus on dissemination and implementation (D&I) research methods and outcome assessment as important skills for researchers and CRPs. Multiple clinical research academic degree and training programs have embraced the JTF Framework as a curriculum standard (Sonstein et al., 2024) and a formal programmatic accreditation process is now available through the Commission on Accreditation of Allied Health Education Programs (2024). Process efficiencies in centralizing new hire JTF competency-based onboarding with on-demand online education (Cranfill et al., 2023). Finally, digital badge micro-credentialing have been tested and are available for replication in other institutions and resource settings (Lee-Chavarria et al., 2024).Evaluating the impact of CRP onboarding, training and education programs and employee performance, satisfaction and retention can include a variety of performance metrics and interpretive feedback that permits capture of the lived experience of CRPs navigating the new complexities of innovative trial designs and research outreach. Sundquist and colleagues (2023) used the JTF Framework to implement and evaluate training and performance metrics for the Canadian Cancer Center Network programs. The Competency Index for Clinical Research Professionals (CICRP) was piloted as one of the many tools used to evaluate an academic education program in clinical research (Jones et al., 2024) HAS is employed at Staffordshire University and declares no conflict of interest.BEB is employed by the Brigham and Women's Hospital, is co-chair of the JTF task force, and declares no financial conflicts of interest.EJ is employed at Montana State University and is partially funded by the Genentech Innovation Fund and the Center for American Indian and Rural Health Equity.DS is employed at Duke University and is partially funded by the NIH, National Center for Advancing Clinical and Translational Science (NCATS) grant UL1TR002553.The other authors declare that the work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The author(s) declared that they were an editorial board member of this Frontiers collection, at the time of submission. This had no impact of the peer review process and final decision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".