## 2 Background and Review
### Research Gaps and Contributions  
Existing evaluation approaches primarily rely on comparisons of pre- and post-training survey responses, which are often subjective and limited in scope [25]. This narrow focus on outcome variables makes it difficult to capture sustained or structural changes in scholars' academic trajectories. To address this limitation, some studies have adopted more objective metrics, such as publication records and grant acquisition. It was found that participation in a clinical and translational research Master of Science degree program was associated with significantly higher post-training publication output, using publication counts as a measure of training effectiveness [26]. [PERSON] et al. [27] reported that graduates of the same program were substantially more likely to receive NIH funding than non-participants, with grant awards serving as another key metric. These findings highlight the value of research outputs in assessing long-term program effectiveness. However, such studies often focus narrowly on quantitative measures like publication counts while overlooking how training programs may influence scholars' research interests and collaborative patterns. This oversight is particularly relevant for interdisciplinary research training programs such as mHTI, where shifts in research focus and collaboration dynamics are critical outcomes. Regarding research specifically focused on the mHTI program, [PERSON] et al. [9] employed Separable Temporal Exponential Random Graph Models (STERGMs) to model the intra-program communication networks of three annual cohorts of mHTI participants from 2017 to 2019, providing recommendations to enhance interdisciplinary scholar interactions. However, such analyses primarily focus on collaboration dynamics during the short-term training program, rather than examining how the program influences scholars' long-term research interests beyond their immediate networks.  
The emergence of LLMs and other advanced computational techniques has facilitated large-scale text analysis, offering new ways to uncover nuanced semantic structures within academic literature. These methods provide novel avenues for evaluating the broader intellectual and collaborative shifts resulting from training programs like mHTI. Motivated by these opportunities, our current study addresses these gaps by combining direct academic outcome data from mHTI participants (i.e., publications) with state-of-the-art techniques to provide a comprehensive analysis of the thematic landscape of mHealth research and the impact of interdisciplinary training programs. The proposed analytic framework is shown in Figure 1. By leveraging methods such as BERTopic modeling and LLM-based topic classification, this study aims to showcase the diversity of research topics conducted by interdisciplinary mHTI scholars, assess the relative significance of mHealth-related publications, and evaluate the influence of the mHTI program on scholars' research trajectories. Furthermore, the proposed framework for tracking scholars' research interest shifts is fully adaptable and can be transferred to the evaluation of other interdisciplinary training programs. This generalizability enables broader applications of our approach in assessing the long-term intellectual impact of training initiatives across domains. Furthermore, the proposed measurement framework for tracking scholars' research interest shifts is fully adaptable and can be transferred to the evaluation of other interdisciplinary training programs.