Back

Student self-assessment: feasibility, advantages and limitations Example of a workshop for trainee surgeons using a suture score

Riquin, E.; Le Nerze, T.; Goulin, J.; Rony, L.; Boucher, S.; Martin, L.; Schmitt, F.

2024-06-04 medical education
10.1101/2024.06.04.24308019 medRxiv
Show abstract

IntroductionEvaluating skills requires developing relevant grading tools that could potentially be used for self-assessments. The goal of our study was to assess the relevance of a self-assessment (SA) grid used for a skin suture exercise as compared to an evaluation carried out at the same time by an expert (EE), while also identifying any factors likely to influence it. Materials and methodsBetween 2018 and 2022, an assessment grid for a skin suture exercise was used during skills assessment sessions with first-year trainee surgeons. The students were given a self-assessment grid to fill out at the same time. The results of the SA and the EE in this grid were analyzed and compared, then set against the anxiety scores of the trainees based on the STAI Y-A. A comparative analysis of trainees with a tendency to over- or underestimate themselves was carried out to identify the factors that could contribute to them misjudging their skills. FindingsThe data for 70 trainee doctors was analyzed. The mean of the difference in score between the SA and the EE was 2 points lower for SA than EE (p < 0.001). The mean score for the STAI Y-A was 36.2 +/- 8.9 and the learners anxiety had no effect on the EE but did affect the SA. When learners showed low self-confidence, they underestimated themselves, but examiners also had a tendency to give them lower grades. In multivariate analysis, the less experience a learner had, the more they tended to overestimate their abilities. ConclusionAnxiety and self-confidence affect self-assessment, but external assessment does not seem to be affected by the learners stress levels. Formative self-reflection based on the use of SA grids should be introduced during medical studies so as to adjust the level of technical skill perceived by trainees and to make simulation-based training more effective.

Published in BMC Medical Education (predicted rank #1) · training set

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.