Seeing with greater clarity: Stakeholder ratings of blind spots in U.S. medical education
Bibliographic record
Abstract
Background Although medical education is affected by numerous blind spots, there is limited evidence to determine which blind spots to prioritize.Methods In summer 2022, we surveyed stakeholders from U.S. medical education who had identified 9 domains and 72 subdomains of blind spots. Respondents used 4-point Likert-type scales to rate the extent and magnitude of problems caused for each domain and subdomain. Respondents also provided comments for which we did content analysis.Results A total of 23/27 (85%) stakeholders responded. The majority of respondents rated each blind spot domain as moderate-major in both extent and problems they cause. Patient perspectives and voices that are not heard, valued, or understood was the domain with the most stakeholders rating extent (n = 20, 87%) and problems caused (n = 23, 100%) as moderate or major. Admitting and selecting learners likely to practice in settings of highest need was the subdomain with the most stakeholders rating extent (n = 21, 91%) and problems caused (n = 22, 96%) as moderate or major. Respondents’ comments suggested blind spots may depend on context and persist because of hierarchies and tradition.Discussion We found blind spots differed in relative importance. These data may inform further research and direct interventions to improve medical education.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".