Blind spots in medical education – International perspectives
Bibliographic record
Abstract
BACKGROUND: All individuals and groups have blind spots that can create problems if unaddressed. The goal of this study was to examine blind spots in medical education from international perspectives. METHODS: From December 2022 to March 2023, we distributed an electronic survey through international networks of medical students, postgraduate trainees, and medical educators. Respondents named blind spots affecting their medical education system and then rated nine blind spot domains from a study of U.S. medical education along five-point Likert-type scales (1 = much less attention needed; 5 = much more attention needed). We tested for differences between blind spot ratings by respondent groups. We also analyzed the blind spots that respondents identified to determine those not previously described and performed content analysis on open-ended responses about blind spot domains. RESULTS: There were 356 respondents from 88 countries, including 127 (44%) educators, 80 (28%) medical students, and 33 (11%) postgraduate trainees. At least 80% of respondents rated each blind spot domain as needing 'more' or 'much more' attention; the highest was 88% for 'Patient perspectives and voices that are not heard, valued, or understood.' In analyses by gender, role in medical education, World Bank country income level, and region, a mean difference of 0.5 was seen in only five of the possible 279 statistical comparisons. Of 885 blind spots documented, new blind spot areas related to issues that crossed national boundaries (e.g. international standards) and the sufficiency of resources to support medical education. Comments about the nine blind spot domains illustrated that cultural, health system, and governmental elements influenced how blind spots are manifested across different settings. DISCUSSION: There may be general agreement throughout the world about blind spots in medical education that deserve more attention. This could establish a basis for coordinated international effort to allocate resources and tailor interventions that advance 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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".