Optimizing the Learner’s Role in Feedback: Development of a Feedback-Preparedness Online Application for Medical Students in the Clinical Setting
Bibliographic record
Abstract
Feedback is an essential component of medical education, especially during clinical rotations. There is growing interest in learner-related factors that can optimize feedback's efficiency, including goal orientation, reflection, self-assessment, and emotional response. However, no mobile application or curriculum currently exists to specifically address those factors. This technical report describes the concept, design, and learner-based feedback of an innovative online application, available on mobile phones, developed to bridge this gap. Eighteen students in their third or fourth year of medical school provided comments on a pilot version of the application. The majority of learners deemed the module relevant, interesting, and helpful to guide reflection and self-assessment, therefore fostering better preparation before an upcoming feedback session. Minor improvements were suggested in terms of content and format. The learners' initial positive response supports further efforts to engage in validity and evaluation research. Future steps include modifying the mobile application based on learners' comments, evaluating its efficacy in a real clinical setting, and clarifying whether it is most beneficial for mid-rotation or end-of-rotation feedback sessions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".