STRENGTHS AND LIMITATIONS OF THIS STUDY

The study uses a robust Delphi method and questionnaire survey to construct a competency model, enhancing the model’s relevance and authority in the Chinese context.

Advanced statistical analyses, including factor analysis and the analytic hierarchy process, are applied to ensure the model’s scientific rigour and reliability.

The research provides a pioneering framework specifically tailored for Chinese Clinical Research Coordinators (CRCs), which is a significant contribution to the professional development and standardisation of the CRC industry in China.

The use of convenience sampling might affect the generalisability of the findings, potentially limiting the representation of China’s full regional diversity.

While the model has been validated through statistical analysis, further empirical testing across various clinical research settings in China is needed to confirm its broader applicability.

Introduction

Clinical trials are an essential phase in drug development, with the efficacy and safety of new drugs ultimately confirmed through human clinical trials.1 As key roles in clinical trials, Clinical Research Coordinators (CRCs)2 3 assist investigators with non-medical judgement related tasks, such as trial management and daily coordination activities, acting as a bridge between clinical researchers, sponsors and participants.4–7 In China, investigators face particularly heavy daily clinical work, which often makes it difficult to devote enough time to the entire clinical trial. CRCs, as assistants to investigators, have become an indispensable role in clinical trials. With the rapid growth in the number of clinical trials in China, the role of CRCs has become increasingly significant.

However, unlike their counterparts in the USA and Europe, where CRCs undergo formal training and certification processes, CRCs face challenges due to the lack of a standardised training and certification system in China.

In Europe and the USA, CRCs, as a profession, need to undergo formal training courses provided by recognised professional institutions or universities before entering the industry. After graduation, they can participate in clinical trials. Two years into their careers, they are required to take professional qualification examinations organised by organisation like the Society for Clinical Research Associates8 or the Association of Clinical Research Professionals.9 10 Currently, these two organisations conduct CRC certification examinations in 15 countries and regions in Europe, the Americas and Asia, with a mandatory requirement of at least 2 years of CRC work experience for examination candidates. Throughout their careers, CRCs must continuously attend workshops and training sessions organised by professional institutions or universities, covering clinical trial skills, medical ethics, pharmacy and specialised knowledge, among other subjects. Regular continuing education is necessary to enhance job capabilities. In contrast, there is currently no unified management standard or model for CRCs in China. Both regulatory oversight and industry norms lack clear and unified requirements and entry standards. Moreover, as CRCs typically come from different Site Management Organisations, the diversity in their origins leads to a disparity in personnel quality. The industry lacks specialised training, and assessment system at the national level. The professional recognition of CRC practitioners is insufficient, and their career development paths are not clearly defined, leading to a high turnover rate. This poses a challenge to the management and efficiency of clinical trial institutions. During the evaluation process of clinical trials, there is seldom consideration given to the competencies of CRCs and suggestions to improve their competencies. This leads to a lack of targeted training and guidance, which further exacerbates the turnover of CRCs and has a detrimental impact on the overall quality of clinical trials.11

China’s unique cultural and regulatory environment requires a tailored model for assessing the competencies of CRCs, which existing frameworks like The Joint Task Force for Clinical Trial Competency model12 13 may not adequately address with the required specificity for the Chinese healthcare context. Specifically, the vast geographical spread of the country leads to significant disparities in education and healthcare systems between urban and rural areas, which in turn influence the accessibility and delivery of clinical trials. Urban centres typically offer more advanced medical facilities and a higher concentration of healthcare professionals, while rural areas might face limitations in infrastructure and resources. Moreover, the Chinese healthcare system is characterised by its own set of regulations, policies and cultural norms that shape the conduct and management of clinical research. For instance, the hierarchical structure of medical institutions, the operation of the healthcare insurance system, and the specific regulatory pathways for drug and device approvals are all context-specific factors that CRCs must navigate. Furthermore, the cultural values and social dynamics in China can significantly affect patient recruitment, informed consent processes and the ethical considerations of clinical trials. These cultural factors, along with the unique educational background and professional development needs of CRCs in China, highlight the importance of a competency model that is not only context-specific but also sensitive to regional variations and local practices.

Therefore, the existing JTF model, while valuable in its general approach to clinical research competencies, may not fully capture the nuances of working within China’s healthcare system, including the rural-urban divide and the specific regulatory and cultural demands. The need for a competency model for CRCs tailored to the Chinese context is evident. This study aims to construct a competency model for CRCs that is not only reflective of the current clinical research environment in China but also serves as a benchmark for the professional development, training and evaluation of CRCs in the country.

MethodsDesign and procedures

Our study was conducted from January to December 2023, and the construction of the CRC general competency model combined the advantages of qualitative and quantitative analysis, divided into three steps. Online supplemental figure illustrates the technical workflow of our study.

Competency characteristic system

Initially, we conducted a comprehensive literature review using keywords such as “Clinical Research Coordinator”, “Study Nurse”, “competency model” and “Delphi method”, in databases including PubMed, Web of Science, the China Biomedical Literature Database and CNKI (China National Knowledge Infrastructure). The review focused on existing competency frameworks and studies related to CRCs, particularly within the Chinese context.

The process of identifying the key competency indicators for CRCs in China was grounded in Competency-Based Theories, which emphasise characteristics significantly correlated with job performance, such as knowledge, skills, abilities, traits and motivations.14 To systematically categorise these characteristics, we applied the Onion Model Theory. This model metaphorically describes competencies in layers, from the outermost layer representing basic knowledge and skills, through the middle layer encompassing attitudes and values along with social roles and self-perception, to the innermost layer reflecting personality and motivation—the deeper, core competencies that are more challenging to cultivate, assess and modify.15 In alignment with the Onion Model, we preliminarily categorised the CRC competencies into five primary indicators, corresponding to the different layers of the model: Knowledge, Skills, Attitudes/Values, Personality and Motivation.

To develop a deeper understanding of the roles and competencies of CRCs in China, we conducted semi-structured interviews with 15 CRCs who have more than 5 years of clinical research experience. The interviews aimed to explore the actual clinical competencies and challenges faced by CRCs in their daily work. The interviews lasted for 60–120 min, continuing until information saturation was reached, and no new information emerged. Core questions included, ‘In the process of clinical trials, what qualities and abilities do you believe CRCs should possess, and which ones are the most important?’; ‘In clinical trials or the assessment of CRC work, what factors are typically considered to evaluate whether a CRC is outstanding?’; ‘When conducting interviews during projects or personnel recruitment for CRCs, what points are taken into consideration, and which do you believe are essential and closely related to hiring decisions?’. The semi-structured interviews were digitally recorded and transcribed verbatim. We used thematic analysis approach to analyse the interview data, organised and summarised the competency characteristics of CRCs, and integrated them with the characteristics previously identified through literature review.

Construction of initial CRC competency model

Subsequently, based on the aforementioned competency characteristic system, the Delphi method was employed to reach a consensus among experts regarding the competency model for CRCs. The typical number of experts in a Delphi survey usually falls between 10 and 50 individuals.16 17 For this study, the Delphi survey expert panel was specifically constituted from the clinically advanced eastern region of China, comprising a select group of 16 experts (online supplemental table 1). Inclusion criteria comprised: having more than 5 years of experience in drug clinical trials, possessing rich operational experience in drug clinical trials, being familiar with the job responsibilities and work content of CRCs, and having a high level of interest and enthusiasm for the content of this consultation, as well as the ability to continuously and completely complete the survey and consultation of this study. The Delphi questionnaire was designed to assess the importance, feasibility and sensitivity of each competency indicator using a 5-point Likert scale.18 After the first round of consultations, we analysed the results based on the mean scores and the coefficient of variation (CV) for each indicator across the three dimensions. Indicators were retained, modified or deleted according to the established criteria: if two or more dimensions had a mean score of ≤3.5 or a CV ≥25%, the indicator was considered for deletion. If only one dimension met these criteria, it was discussed further with the experts for a decision on modification or retention. An indicator was included if it had a mean score >3.5 and a CV <25% across all three dimensions.19 The second round of the Delphi process incorporated the feedback from the first round, and the questionnaire was revised accordingly. The same experts were invited to provide their input again, ensuring continuity and a thorough examination of the competency model. The process continued until a consensus was reached among the experts, indicating a stable and agreed-upon set of competency indicators for CRCs in China.

Establishment of the final CRC competency model

Third, to avoid sequence bias, we randomly arranged the items in the initial competency model established by the Delphi panel, and assessed the importance of each item using a Likert scale. This study conducted a convenient sample of CRCs within clinical trial teams. Participants were sourced from CRCs working in drug clinical trial institutions approved by the National Medical Products Administration across 30 provinces in mainland China. According to the sample size calculator, the minimum estimated sample size Raosoft for this survey was 377, using the formula, where n is the required sample size, N is the population size, x is the CI, assuming a 95% CI and E is the margin of error at 5%: n=N×x/((N−1) E2+x).20 Two trained research assistants from each province distributed the online questionnaire to potential participants. Participants completed the online survey through the ‘Survey Star’ online survey platform (Changsha Ranxing Technology, China). The questionnaire was completed anonymously, with no personal identification information. All participants were informed about the study before accessing the online questionnaire and provided consent before commencing. Participants were asked to rate items on a 5-point Likert scale, where 1–5 represented ‘not important at all’, ‘unimportant’, ‘neutral’, ‘important’ and ‘very important’, respectively. The questionnaire data underwent tests for reliability and validity to validate the scientific and reliable nature of the competency model, ultimately resulting in the final CRC competency model.

Patient and public involvement

This study did not involve patients or the public in the development of the research question, design, recruitment or conduct of the study. The research was focused on constructing a competency evaluation model for CRCs in China. Results will be disseminated through academic channels. There were no patient advisers involved in this study.

Statistical analysis

Data management and analysis were conducted using a combination of software tools to ensure robust and accurate results. Microsoft Excel was used for preliminary data organisation, while SPSS software V.18.0 was employed for advanced statistical analysis. In the Delphi study, Kendall’s coefficient was employed as a parameter to assess the consistency of opinions among different experts. To calculate weights and to reflect the relative importance of each indicator, we used the analytic hierarchy process (AHP) and the combination weighting method. These weights determined through AHP provide a reference value for the application of the competency model. Subsequently, we employed survey data to test the reliability and validity of the competency model. Cronbach’s α coefficient, commonly used to assess the internal consistency of a scale, was used to examine the internal consistency of both the entire questionnaire and the four primary indicators. An α coefficient above the standard threshold of 0.7 indicates good internal consistency and reliability. Simultaneously, we conducted exploratory factor analysis (EFA) to explore the underlying factor structure of the competency model. This was followed by confirmatory factor analysis (CFA) using AMOS software V.22.0, which allowed us to test the effectiveness of our model. The CFA results were evaluated using a set of goodness-of-fit indices, which are recommended for assessing the model’s adequacy. These indices included the χ2/df ratio(CMIN/DF), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Normed Fit Index (NFI), Incremental Fit Index (IFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA). A well-fitting model is generally indicated by values close to or exceeding the recommended thresholds: CMIN/DF <3, GFI, AGFI, NFI, IFI, TLI and CFI >0.9 and RMSEA <0.08.21 22 A significance level of p<0.05 was deemed statistically significant.

ResultsSemistructured interviews

The interviews conducted indeed captured narratives that reflect the unique aspects of Chinese culture, healthcare and clinical trials management. These narratives provided insights into the challenges faced by CRCs. Through integration and optimisation, the preliminary results of the discussions led to the formation of 5 primary indicators, 22 secondary indicators and 61 tertiary indicators along with their explanations. The demographic data of the interviewees can be found in online supplemental table 2.

Delphi panels

After two rounds of Delphi consultations, a consensus was reached among the panel members. The Kendall’s coefficient of concordance for the importance, feasibility and sensitivity of the indicators in the first round were 0.315, 0.244 and 0.202, respectively (p<0.001). In the second round, the coefficients were 0.297, 0.306 and 0.212 (p<0.001), confirming the consensus among experts. Based on the outcomes of the first round, 22 tertiary indicators were removed. Additionally, four tertiary indicators were merged into two (‘adjustment ability’ and ‘adaptability’ into ‘adjustment and adaptability’, ‘analytical ability’ and ‘evaluation ability’ into ‘evaluation and analysis ability’), and ‘research-related knowledge’ was split into ‘product knowledge’ and ‘protocol knowledge’. This process resulted in a refined set of 5 primary indicators, 21 secondary indicators and 38 tertiary indicators that were carried forward to the second round. In the second round, the primary indicator ‘Motivation’ was further removed due to its mean scores in feasibility and sensitivity being <3.5. Ultimately, the CRCs competency model was established with 4 primary indicators, 20 secondary indicators and 37 tertiary indicators, which are presented in the final model. Then we used the AHP and combination weighting to calculate the competency indicators’ weights. In the primary indicators, ‘Knowledge’ weight is the highest at 0.4715, followed by ‘Attitudes/Values’ with a weight coefficient of 0.2550, ‘Personality’ with a weight coefficient of 0.1653 and ‘Skills’ with the lowest weight, at 0.1083. The weight results for the secondary and tertiary indicators can be found in table 1.

Table 1

Competency model hierarchical indicators and their weights

Survey participants and questions

From May to October 2023, a total of 600 questionnaires were distributed, resulting in 546 completed questionnaires. After excluding invalid questionnaires (due to incomplete data), a total of 491 valid questionnaires were obtained, with an effective response rate of 81.83%. The demographic characteristics of the survey participants are presented in table 2.

Table 2

General demographic characteristics of respondents

Questionnaire reliability and validity analysis

This study identified four core factors of CRC competencies: Knowledge, Skills, Attitudes/Values and Personality. A reliability analysis was conducted for each factor (table 3). The Cronbach’s α coefficients exceeded the benchmark of 0.7 for all factors, indicating good internal consistency and reliability. The corrected item-total correlation for all items surpassed the threshold of 0.5, indicating that the measured items meet the research requirements. Further analysis showed that the deletion of any item would not increase the Cronbach’s α value, substantiating the reliability of the variables.

Table 3

Reliability analysis

For the factor analysis, the data set was randomly split into two groups. The first group was subjected to EFA to examine the underlying factor structure of the competencies. With a Kaiser-Meyer-Olkin measure of 0.972 and a significant Bartlett’s Test of Sphericity (p<0.001), the suitability of the questionnaire for EFA was confirmed. Using the principal component extraction method and following a maximum orthogonal rotation, four distinct factors were identified, each with factor loadings exceeding 0.5 (table 4). These factors accounted for 30.933%, 12.279%, 10.494% and 9.387% of the variance, respectively, culminating in a cumulative explained variance of 63.093%. The EFA results were statistically significant.

Table 4

Rotated component matrix

The second data group underwent CFA to verify the structural model. Employing AMOS software for the CFA, the CFA model diagram is shown in figure 1, and the goodness-of-fit indices are as follows: CMIN/DF=1.541, GFI=0.903, AGFI=0.891, NFI=0.925, IFI=0.972, TLI=0.970, CFI=0.972 and RMSEA=0.033. All these indices meet the model fit standards, demonstrating a good fit for the model.

Figure 1

Results of confirmatory-factor analysis of general competency model for Clinical Research Coordinator.

Discussion

The development of the CRC competency model in this study addresses a significant gap in the Chinese clinical research context. Given the rapid increase in clinical trials, the role of CRCs has become not only pivotal but also increasingly complex. This necessitates a structured and scientifically rigorous approach to defining and evaluating their competencies, which our model aims to provide.

The competency model developed in this study is underpinned by a rigorous scientific approach. It incorporates a comprehensive literature review and expert consultations, ensuring that the indicators are grounded in both theoretical frameworks and practical insights from the field of clinical research in China. Our model takes into account the unique aspects of China’s healthcare system and cultural nuances, which are crucial for the effective implementation of CRC competencies. It acknowledges the unique cultural and systemic factors that influence the role of CRCs, making it a more relevant tool for assessing competencies in this setting compared with generic or Western-centric models.

Our CRC competency model has been thoughtfully designed to take into account the unique aspects of Chinese culture, with targeted considerations in various respects. The ‘Attitudes/Values’ primary index within our model places special emphasis on ‘Integrity’, reflecting the high regard for ethical conduct and compliance within the context of clinical research in China.23 Additionally, the indices of ‘Patient Education Skills’ and ‘Subject Focus’ embody a patient-centred approach that aligns with the expectations of healthcare in China. The ‘Professional Knowledge’ index includes a deep understanding of medical practices and policies in China, ensuring that CRCs can adapt to the local medical environment. Furthermore, the ‘Process Knowledge’ index covers site and subject management processes that are consistent with the regulatory requirements for clinical trial management in China. The ‘Language Proficiency’ index recognises the importance of effective communication across the diverse linguistic backgrounds present in China. The indices of ‘Adaptability and Adjustment’ and ‘Boundary Awareness’ highlight the need for CRCs to possess flexibility and a clear understanding of professional boundaries within the cultural and healthcare context of China. At the same time, the ‘Legal and Regulatory’ index ensures that CRCs have a profound understanding of China’s unique laws and regulations, which is crucial for protecting the rights and interests of subjects and the compliance of clinical trials.24 Through these comprehensive considerations of indicators, our model not only respects Chinese cultural values and social expectations but also adapts to the actual situation of China’s medical system and regulatory environment.

In comparison with existing models, such as the internationally used JTF competency model,25 our model distinctly emphasises familiarity with Chinese laws and regulations, a patient-centred approach, and a high valuation of ethical behaviour and compliance. In addition, considering the uneven distribution of public medical resources in China’s medical system, our model enhances the assessment of CRCs’ ability to work in medical institutions at different levels, ensuring that they can effectively coordinate clinical research in a diverse medical environment. We particularly emphasise ‘Patient Education Skills’ and ‘Subject Focus’ to reflect the respect for patients and patient-centred medical methods in Chinese culture. In addition, our model gives higher weight to ‘Integrity’ in the ‘Attitudes/Values’ aspect, reflecting the strict requirements of China’s clinical research on ethics and regulatory compliance.

Although our model does not explicitly use the term ‘research ethics’, it integrates the concept of research ethics and subject protection through specific third-level indicators. For instance, the ‘Research Related Knowledge’ indicator stresses the importance for CRCs to understand the trial’s purpose, target population, procedures and observation indicators, as well as the fundamental components and key points of informed consent, which are integral to research ethics. Our model includes considerations for ‘Centre Management Processes’, such as research institution establishment, ethical review processes, contract review and signing procedures and safety event reporting processes, all of which are pivotal for the protection of research ethics and subject rights. Furthermore, the ‘Subject Management Process’ is a pivotal component of our model, highlighting the significance of CRCs’ management and attention to subjects during the trial process. A robust subject management process is essential not only for safeguarding the ethics and regulatory compliance of clinical trials but also for enhancing subject participation and ensuring the accuracy of trial data. In addition to the subject management processes, our model places considerable emphasis on the indicators of ‘Legal and Regulatory’, ‘Legal Adherence’ and ‘Compliance’. These indicators reflect the critical role that CRCs’ knowledge of and adherence to laws and regulations play in ensuring the ethical conduct of clinical trials. A thorough understanding and strict compliance with legal frameworks are as vital as the management processes themselves, providing a dual foundation for the ethical and compliant execution of clinical research.

Strengths and limitations of this study

While the CRC competency scale developed in this study demonstrates strengths in construction methods, content coverage, reliability and validity, there are inherent limitations that should be acknowledged. Potential regional or institutional biases in the sample, incompleteness of model dimensions and scale items, and insufficient validation of the model highlight the need for cautious application and future refinement and validation in subsequent research. The reliance on a convenience sample may limit the generalisability of the findings. Future research should aim to diversify the sample and further validate the model across various clinical research settings in China.

Conclusion

This study marks the inaugural development of a competency model tailored for Chinese CRCs, accompanied by a reliable and valid assessment scale. The findings bear significant implications for the recruitment, training, development and management of CRCs. The results offer valuable guidance for CRCs in their professional development, aiding them in acquiring essential knowledge and skills. Moreover, they provide reference points for CRC managers, assisting in the selection and cultivation of CRC talent. In the long run, these advancements contribute to the professionalisation and standardisation of clinical research, ultimately enhancing the quality of healthcare services and patient health outcomes.

Data availability statement

No data are available.

Ethics statementsPatient consent for publication

Not applicable.

Ethics approval

Ethical approval for this study was provided by the Ethics Committee of Nanjing Medical University, China (Approval No. (2021)103). The experts were fully informed of the purpose, significance, research contents and methods of the study. The freedom and rights of the participants to participate in or withdraw from the study were respected, and their interests were protected. To protect the experts from any consequences, data were made anonymous before analyses.

Acknowledgments

The authors wish to thank all participants, especially the 16 experts who helped with the analysis and interpretation of this study.