Picture a learner halfway through a clinical year. Every rotation evaluation reads "meets expectations." One preceptor calls them a pleasure to work with. Another writes "continue to build confidence." An advisor notes the learner seemed a little quiet back in October. Someone on the coordinating team remembers a late assignment, though nobody's quite sure which one.
Is this learner struggling?
That was the opening question at our recent Learner Progress webinar, and it's harder than it looks. Some people will say obviously not, everything's passing. Others will say obviously yes, look at the pattern. Both are reasonable answers, which is exactly the problem. Hand that same file to five people and you might get five different calls.
Here's the idea we kept circling back to: most programs aren't short on information about their learners. They're short on an agreed moment where that information becomes a decision. Below are the pieces of the session worth carrying back to your committee.
Define what "struggling" means
"Struggling," "off track," "at risk," whatever your program calls it, needs a shared definition before anyone builds a report or a review process around it. A recent scoping review of 14 remediation reviews in health professions education found that definitions of a struggling learner either varied from study to study or were never stated at all (Percival et al., 2026). If the research community hasn't settled this, it's no surprise your advisors, course directors, and committees might be working from slightly different versions of the same word. That's a design problem, and design problems are fixable. Write the definition down, use it consistently, and frame it as a trigger for a conversation rather than a label for a person.
A dashboard makes data more visible, but it doesn't make the call
"Can we get a report that just shows us who's at risk?" is one of the most common requests we hear, and it's a reasonable thing to want. Evidence about a learner arrives from different sources, methods, contexts, and time periods, and pulling all of that into one place is hard to do by hand. That part is solvable.
But once everything is assembled in front of the right people, somebody still has to decide. Researchers who sat in on two real competency committees found their decisions were shaped less by data quality and more by who was in the room, how the group leaned, and members' personal history with the learner (Curtis et al., 2023). Hand that same committee a perfect dashboard, and all of that is still sitting at the table. A system can assemble the evidence. It can't account for the nuance of real people sitting on a competency committee with differing knowledge, experiences, and roles.
Comments are the most important element to consider
Specific, written feedback produces better decisions than vague feedback does, and a study that deliberately manipulated the quality of narrative comments in student portfolios backs this finding (de Jong et al., 2022). So why don't assessors write more of it? Because writing something specific can feel like starting a process you can't stop, especially when it comes to critical feedback. Studies of clinical supervisors keep finding the same barriers: not knowing what to document, fear of what a comment might trigger, and no clear next step once it's written (Yepes-Rios et al., 2016; Dudek et al., 2005). So assessors default to something like "continue to build confidence," a sentence everyone understands, but one that nobody has to act on. No dashboard recovers a decision that was already softened at the source.
Be careful who sees the comments
The instinctive fix for concerns that surface too late is to share them earlier and more widely. However, the evidence says to be careful with that instinct. In one study, reviewers told in advance that a learner had struggled to rate the same recorded encounter lower than reviewers told the learner had excelled, even after most had worked out what the study was testing for (Shaw et al., 2021). Noticing something early is good. Broadcasting it without a plan for who sees it, and when, can quietly put a thumb on the scale for the next assessment that takes place.
What programs can do next
Start with a decision inventory. List the progress decisions your program makes, and what evidence each one needs to support the decision-making process. Then, take an honest look at what you're asking assessors to write, and make it a little safer for them to write it plainly. Neither requires new software. Both just require deciding, on purpose, what "struggling" means before the next committee meeting. Platforms like Elentra can help make that evidence easier to see and filter once you know what you're looking for, but the definition still has to come from your program.
Access the webinar recording, including the live poll results and discussion of the four things that researchers keep finding which shape a committee's decision regardless of data quality. If your program is part of the Elentra community, please join the conversation on Elentra Connect and share how this resonates when you take it back to your own team. Tell us what your committees already agree on, where it gets messy, and what you'd tell another program asking the same question.
If your program is working to make learner progress decisions more consistent, transparent, and evidence-informed, Elentra can help. By bringing assessment data, narrative feedback, and longitudinal learner information together, Elentra gives faculty and committees a clearer view of the evidence they need to support meaningful review and decision-making. If you'd like to learn how Elentra can help your program better understand learner progress and turn information into actionable insight—contact us today.
References
- Curtis, C., Kassam, A., Lord, J., & Cooke, L. J. (2023). Competence committees decision-making: an interplay of data, group orientation, and intangible impressions. BMC Medical Education, 23, 748. https://doi.org/10.1186/s12909-023-04693-4
- de Jong, L. H., Bok, H. G. J., Schellekens, L. H., Kremer, W. D. J., Jonker, F. H., & van der Vleuten, C. P. M. (2022). Shaping the right conditions in programmatic assessment: how quality of narrative information affects the quality of high-stakes decision-making. BMC Medical Education, 22, 409. https://doi.org/10.1186/s12909-022-03257-2
- Dudek, N. L., Marks, M. B., & Regehr, G. (2005). Failure to fail: the perspectives of clinical supervisors. Academic Medicine, 80(10 Suppl), S84-S87. https://doi.org/10.1097/00001888-200510001-00023
- Percival, C. S., Wyatt, T. R., Martin, P. C., & Maggio, L. A. (2026). Remediation in health professions education: a scoping review of reviews. Academic Medicine, 101(7), 858-868. https://doi.org/10.1093/acamed/wvag003
- Shaw, T., Wood, T. J., Touchie, C., Pugh, D., & Humphrey-Murto, S. M. (2021). How biased are you? The effect of prior performance information on attending physician ratings and implications for learner handover. Advances in Health Sciences Education, 26(1), 199-214. https://doi.org/10.1007/s10459-020-09979-6
- Yepes-Rios, M., Dudek, N., Duboyce, R., Curtis, J., Allard, R. J., & Varpio, L. (2016). The failure to fail underperforming trainees in health professions education: A BEME systematic review: BEME Guide No. 42. Medical Teacher, 38(11), 1092-1099. https://doi.org/10.1080/0142159X.2016.1215414