Unravelling epistemic injustice in medical education: The case of the underperforming learner
Bibliographic record
Abstract
CONTEXT: Epistemic injustice refers to a wrong done to someone in their capacity as a knower. While philosophers have detailed the pervasiveness of this issue within healthcare, it is only beginning to be discussed by medical educators. The purpose of this article is to expand the field's understanding of this concept and to demonstrate how it can be used to reframe complex problems in medical education. METHODS: After outlining the basic features of epistemic injustice, we clarify its intended (and unintended) meaning and detail what is required for a perceived harm to be named an epistemic injustice. Using an example from our own work on introversion in undergraduate medical education, we illustrate what epistemic injustice might look like from the perspectives of both educators and students and show how the concept can reorient our perspective on academic underperformance. RESULTS: Epistemic injustice results from two things: (1) social power dynamics that give some individuals control over others, and (2) identity prejudice that is associated with discriminatory stereotypes. This can lead to one, or both, forms of epistemic injustice: testimonial and hermeneutical. Our worked example demonstrates how medical educators can be unaware of when and how epistemic injustice is happening, yet the effects on students' well-being and sense of selves can be profound. Thinking about academic underperformance with epistemic injustice in mind can reveal an emphasis within current educational practices on diagnosing learning deficiencies, to the detriment of holistically representing its socially constructed and structural nature. CONCLUSIONS: This article builds upon recent calls to recognise epistemic injustice in medical education by clarifying its terminology and intended use and providing in-depth application and analysis to a particular case: underperformance and the introverted medical student. Equipped with a more sophisticated understanding of the term, medical educators may be able to re-conceptualise long-standing issues including, but also beyond, underperformance.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.037 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.019 |
| 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".