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Enregistrement W4400693768 · doi:10.3389/fphar.2024.1446908

Editorial: Building the clinical research workforce: challenges, capacities and competencies

2024· editorial· en· W4400693768 sur OpenAlexaboutno aff
Carolynn Thomas Jones, Elizabeth A. Johnson, Barbara E. Bierer, Denise C. Snyder, Hazel Smith, Emma Akuffo, Stephen A. Sonstein

Notice bibliographique

RevueFrontiers in Pharmacology · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensnon disponible
Organismes subventionnairesNational Center for Advancing Translational SciencesNational Institutes of HealthOhio State University
Mots-clésWorkforceMedicineLibrary sciencePolitical scienceComputer science

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,040
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,994
Score d'incertitude au seuil0,050

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,040
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,001
Études des sciences et des technologies0,0040,003
Communication savante0,0080,006
Science ouverte0,0030,002
Intégrité de la recherche0,0100,016
Charge utile insuffisante (le modèle a refusé de juger)0,0150,011

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,252
Tête enseignante GPT0,540
Écart entre enseignants0,288 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineIncitatifs
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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