Metacognitive Factors Influencing Training Retention Among Multidisciplinary Medical Staff

Abstract

Aim/Purpose: The purpose of this study was to reduce the professional training dropout rate among multidisciplinary medical staff by identifying and analyzing key factors that influence engagement and retention. Specifically, this study aimed to explore how metacognitive factors affect commitment to completing training programs and to develop a causal model that elucidates the relationships between various metacognitive factors and retention outcomes. The intention was to enhance understanding of how these factors collectively contribute to sustained participation and success in professional training. The study emphasizes metacognition—the awareness, regulation, and control of one’s own learning processes. Thus, it contributes to the growing need for evidence-based models that foster reflective, self-regulated, and adaptive learning behaviors among healthcare professionals, who must constantly update their competencies in response to evolving medical technologies and practices.

Introduction/Background: In recent years, multidisciplinary medical staff have faced increasing challenges in completing professional development training, primarily due to heavy workloads, extended working hours, and the complex nature of responsibilities within healthcare systems. These pressures have contributed to a growing dropout rate from training, raising concerns about the long-term sustainability of continuing professional development in the healthcare workforce. Nevertheless, the constant and rapid evolution of medical technologies and field practices necessitates that healthcare professionals remain up to date with emerging knowledge and competencies. Ongoing training is critical not only for maintaining field proficiency but also for ensuring high-quality patient care. To be both effective and sustainable, the training must be designed with consideration for the practical constraints and professional expectations encountered by medical staff. Accordingly, this study aimed to investigate the key factors that influence training retention, with the goal of finding those that enhance the continuity and success of professional training among multidisciplinary medical staff.

Methodology: In this study, a quantitative research design was employed utilizing Confirmatory Factor Analysis (CFA) to examine the proposed model. An online questionnaire was developed based on the concept of metacognition and reviewed by subject matter experts to ensure content validity. Data were collected for three months from 230 multidisciplinary medical staff, recruited through snowball sampling techniques. CFA was performed using the AMOS program, and model fit was assessed through multiple standard indices, including the Chi-square statistic, p-value, Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), and Root Mean Square Residual (RMR), to confirm the adequacy of the measurement model.

Findings: The results showed that six factors influenced the retention and successful completion of professional training among multidisciplinary medical staff: life balance, career advancement, curriculum quality, training methods, organizational support, and interpersonal relationships. The model demonstrated a satisfactory fit to the empirical data, with statistical values as follows: Chi-square = 249.612, df = 225, p = .125, GFI = .900, AGFI = .882, CFI = .993, and RMR = .026. Although the Chi-square value was not significant (p = .125 > .05), the additional fit indices—all of which reflected a strong overall model fit to the empirical data—indicated that the proposed model adequately represented the observed data. These findings highlight the importance of incorporating these factors into training program design to enhance effectiveness and support long-term professional development in multidisciplinary medical staff.

Contribution/Impact on Society: This study contributes to the growing body of knowledge on professional development in the healthcare sector by identifying six factors—life balance, career advancement, curriculum quality, training methods, organizational support, and interpersonal relationships—that significantly influence training retention among multidisciplinary medical staff. The findings offer practical insights for training program designers seeking to improve workforce stability and elevate the overall quality of healthcare services. By addressing these factors, healthcare institutions can enhance staff engagement and reduce professional training drop-out rates, ultimately benefiting workforce knowledge and organizational performance.

Recommendations: Training programs should incorporate flexible training structures that support work-life balance and align with professional growth trajectories. Emphasis should be placed on curriculum quality, supportive policies, and strong interpersonal connections among trainees and mentors. Integrating technological innovations—such as e-training platforms, AI-based adaptive systems, and online reflective tools—can further enhance accessibility, motivation, and engagement. The use of digital environments that promote reflection and peer interaction is recommended to improve long-term retention and satisfaction.

Research Limitation: The primary limitation was the use of snowball sampling, which while practical for reaching diverse healthcare professionals, inherently limited the representativeness and generalizability of the findings. Additionally, the sample size (n = 230) may not fully have captured institutional diversity, and the reliance on CFA did not examine wider contextual factors such as institutional policies or organizational culture. Despite these limitations, the findings provide a reliable foundation for subsequent model testing and application in broader healthcare contexts.

Future Research: Future research could apply the proposed metacognitive model to multidisciplinary medical staff in various professional and educational training contexts to examine its impact on training retention and engagement. Integrating the model with digital learning tools, adaptive systems, or simulation-based training may further support personalized and sustainable professional development. Longitudinal studies could track the development of metacognitive awareness and self-regulated learning during training, providing insights into how these factors contribute to sustained participation and professional growth.