Blind spots in medical education: how can we envision new possibilities?
Bibliographic record
Abstract
As human beings, we all have blind spots. Most obvious are our visual blind spots, such as where the optic nerve meets the retina and our inability to see behind us. It can be more difficult to acknowledge our other types of blind spots, like unexamined beliefs, assumptions, or biases. While each individual has blind spots, groups can share blind spots that limit change and innovation or even systematically disadvantage certain other groups. In this article, we provide a definition of blind spots in medical education, and offer examples, including unfamiliarity with the evidence and theory informing medical education, lack of evidence supporting well-accepted and influential practices, significant absences in our scholarly literature, and the failure to engage patients in curriculum development and reform. We argue that actively helping each other see blind spots may allow us to avoid pitfalls and take advantage of new opportunities for advancing medical education scholarship and practice. When we expand our collective field of vision, we can also envision more "adjacent possibilities," future states near enough to be considered but not so distant as to be unimaginable. For medical education to attend to its blind spots, there needs to be increased participation among all stakeholders and a commitment to acknowledging blind spots even when that may cause discomfort. Ultimately, the better we can see blind spots and imagine new possibilities, the more we will be able to adapt, innovate, and reform medical education to prepare and sustain a physician workforce that serves society's needs.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.103 |
| Scholarly communication | 0.027 | 0.043 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 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".