What’s the Best Way to Measure Mentorship?

A great mentor can shape a scientist’s career, and sometimes even their entire future. New metrics are now bringing this aspect into sharper focus, prompting broader conversations about how the impact of mentorship can be defined and measured. The Scientist asked its readers: What are your thoughts on quantifying mentorship?

Ebba Brakenhielm, vascular biologist at the French National Institute for Health and Medical Research (Inserm)

It's a great idea to give some credit to those that take on the task of mentoring students and younger colleagues. Numbers are not everything, though. Perhaps we can give a bonus point to those principal investigators who see their mentees get funding, selection for oral presentations, or prizes of some sort? That could stimulate even more engagement between senior and junior researchers to push towards excellence at all levels.

Girish Mahajan, microbiologist and senior vice president of HiMedia Laboratories Pvt. Ltd.

Mentorship in science is best understood not as a soft virtue, but as a measurable capability-building system. In scientific organizations, the real question is not whether mentoring happened, but whether it changed performance. That is why mentorship should be quantified across three linked levels: engagement, development, and outcome.

Engagement measures the visible inputs—frequency of mentoring discussions, protocol reviews, turnaround time on feedback, troubleshooting support, and goal-setting conversations.

Development measures whether the mentee is becoming more scientifically capable—stronger experimental design, better documentation, cleaner data interpretation, improved scientific writing, and more mature decision-making.

Outcome measures whether that developmental gain is translating into institutional value—faster onboarding, fewer repeat errors, reduced supervision burden, higher productivity, better retention, and readiness for independent project ownership. Evidence from the research mentorship literature consistently links effective mentoring with stronger productivity, greater self-efficacy, and better career satisfaction.

A practical way to quantify mentorship, therefore, is to ask not, “How often did the mentor meet the trainee?” but rather, “What moved because of the mentoring?” Did error rates fall? Did experimental autonomy rise? Did the scientist begin anticipating risks earlier, designing studies with less handholding, and communicating results with greater clarity? In that sense, the best mentorship key performance indicator is not activity volume. It is the rate at which dependence converts into independent scientific judgment. That is the true return on mentoring investment.

A more fitting example from global scientific history is British physicist, J.J. Thomson, who was noted not only for his own discoveries, but for building a research environment from which seven Nobel Prizes emerged among those who worked under him. Ernest Rutherford later extended that legacy. Exceptional mentorship leaves a measurable signature: It multiplies talent, accelerates independence, and produces successors who go on to lead discoveries themselves.

Georgia Lattanzi, postdoctoral researcher at the Salk Institute for Biological Studies.

I think mentorship is fundamental for shaping and supporting the next generation of scientists, and it is quite rewarding for mentors to see the success of their mentees. Mentorship is a vocation, and it shouldn't be mandatory, since a bad mentor is worse than no mentor. But even when we want to mentor, how do we do it? How do we quantify our mentorship? It is difficult to have honest feedback from mentees because they often look at you with total awe. My personal approach is to ask mentees what aspect of my mentoring style worked for them and what didn't, emphasizing that negative feedback is for my own growth. Also, trying to stay in touch with mentees over time, following their work path and maintaining a stable connection is a good way to quantify your mentorship.

Kishore Narasimhan, postdoctoral researcher at Texas A&M University

Mentorship is difficult to quantify because its impact often extends far beyond immediate metrics. Still, a few elements can be meaningfully evaluated: how accessible a mentor is, how consistently they support a trainee’s career growth, how clearly they provide scientific and professional direction, and how effectively they help trainees stay productive without feeling lost or unsupported. A good mentor does more than supervise experiments; they create an environment where trainees can ask questions freely, develop confidence, build independence, and move steadily toward their next career stage. So, while mentorship cannot be reduced to a single number, it can be reflected in the success of the people being mentored.

Responses have been edited for length and clarity.