Exploring Feedback Literacy in the Undergraduate Medical Education Context
Bibliographic record
Abstract
Feedback has long been used and studied in medical education. To acknowledge the complexity of the feedback process, the term feedback literacy has been introduced into the medical education literature. This thesis attempted to explore feedback literacy in the undergraduate medical education context by aggregating a comprehensive body of evidence and using different research methodologies. It focused on providing a more complete understanding of feedback literacy, identifying factors and learning strategies that could improve medical students’ feedback literacy skills, and direct further research on this topic. Results showed that little is known on how to teach feedback literacy and educational interventions to increase students’ feedback literacy skills are still not well established. When exploring factors that could improve students’ feedback literacy skills, this thesis’ results identified that being more intrinsically goal oriented, having strong self-regulated learning traits, and seeking help when needed were positively associated to having better feedback literacy skills. Strategies that students could use to improve their own feedback literacy included self-reflection about the feedback received and how to be more proactive in the feedback process, take small steps when applying the feedback received, and actively discuss the feedback with the giver. Additionally, self-reflections on ones’ motivational beliefs and interests, combined with actions such as creating and implementing strategies to manage motivations, could help students to adjust their learning goal orientation and, consequently, improve their feedback literacy skills. Students should encourage themselves to regulate their learning in the areas of planning, monitoring, and making adjustments in learning strategies to adapt to new situations whenever needed. Lastly, students should seek assistance from others by bringing concerns up, asking questions, and asking clarifications about the feedback received. Taken together, the findings of this thesis support students’ empowerment in the feedback process to help them to make the most of their feedback opportunities in medical school.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.000 |
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 teacher head, 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".