Evaluating and supporting leadership, management, and mentoring: a framework for catalyzing responsible research and healthy research environments - McIntosh, Tristan; Antes, Alison L. (Frontiers in Research Metrics and Analytics, 2025)
Those who lead research teams have myriad roles and responsibilities that are pivotal to both producing rigorous and responsible scientific work and creating a supportive research environment that cultivates this work. We begin by presenting a leadership, management, and mentoring (LMM) framework focused on three critical roles researchers must play that have direct impact on the scientific work, the work environment, and research team dynamics: the role of research leader, research manager, and research mentor. Research leadership involves fostering a healthy research culture by building relationships where team members feel respected and supported. Research management involves providing oversight and direction of day-to-day operations to ensure tasks are done effectively, rigorously, and responsibly. Research mentoring involves providing opportunities and support to team members so that they develop professionally and build their careers. While these three roles are distinct, there is overlap in the professional, interpersonal, and intrapersonal skills that underlie their effective performance, such as communication, active listening, emotion management, and self-reflection. We also draw attention to some of the challenges when performing LMM roles. A variety of sources and types of evaluation measures may be used to comprehensively assess the functioning of a research team and its leader(s). We illustrate key domains for measurement, example indicators of effectiveness in those domains, and examples of the types of measures that could be used for evaluation. We discuss how top-down evaluation, bottom-up evaluation, and self-evaluation methods could be employed for data collection and note that each of these methods has strengths and limitations. We recommend multiple sources and types of data but acknowledge that evaluation must be feasible and practical. We note best practices and key implementation considerations for each method of measurement. When combined, these three methods provide a robust approach for evaluating LMM. We conclude with a description of key considerations for supporting the evaluation and application of LMM in real-world settings at academic institutions. Such considerations include senior leadership buy-in and communication about LMM expectations and providing appropriate framing, time, support, and incentives for LMM. We also highlight institutional risk factors that may inadvertently undermine LMM goals.