Patient Engagement in Medical Education During the COVID-19 Pandemic: A Critical Reflection on an Epistemic Challenge [Version 2]
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Epistemic injustices are defined as power inequalities in the access, recognition and production of knowledge. Their persistence in medical education, especially to the detriment of patients and their specific knowledge, has been documented by several authors. Patient engagement is a new paradigm that involves fostering meaningful patient collaboration at different levels of the healthcare system. Since it is fundamentally based on the recognition of the value and relevance of patients' experiential knowledge, patient engagement in medical education is generally recognized as a desirable strategy to address epistemic injustices in the field. Patient engagement is challenged in the context of COVID-19 where most Canadian medical schools have had to quickly modify their teaching models, stop in-person classes and redirect most activities online. This article presents a critical reflection on the issues raised by COVID-constrained teaching strategies and their impact on epistemic injustices in medical education. It also suggests strategies to favour epistemic justice in medical education despite the pandemic turmoil and online shift. It therefore adds an epistemic perspective to the reflection on the effects of the pandemic on medical education and training, which has been little discussed so far.
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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 | 0.046 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.037 |
| Scholarly communication | 0.030 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.026 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 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".