Bibliographic record
Abstract
BACKGROUND: Despite being recognized as a fundamental part of the educational process and emphasized for several decades in medical education, the influence of the feedback process is still suboptimal. This may not be surprising, because the focus is primarily centered on only one half of the process - the teachers. The learners are the targets of the feedback process and improvement needs to be shifted. Learners need to be empowered with the skills needed to receive and utilize feedback and compensate for less than ideal feedback delivery due to the busy clinical environment. METHODS: Based on the available feedback literature and clinical experience regarding feedback, the author developed 10 tips to empower learners with the necessary skills to seek, receive, and handle feedback effectively, regardless of how it is delivered. Although, most of the tips are directed at the individual clinical trainee, this model can be utilized by clinical educators involved in learner development and serve as a framework for educational workshops or curriculum. RESULTS: Ten practical tips are identified that specifically address the learner's role in the feedback process. These tips not only help the learner to ask, receive, and handle the feedback, but will also ease the process for the teachers. Collectively, these tips help to overcome most, if not all, of the barriers to feedback and bridge the gaps in busy clinical practices. CONCLUSIONS: Feedback is a crucial element in the educational process and it is shown that we are still behind in the optimal use of it; thus, learners need to be taught how to better receive and utilize feedback. The focus in medical education needs to balance the two sides of the feedback process. It is time now to invest on the learner's development of skills that can be utilized in a busy day-to-day clinical practice.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".