The Decline of Basic Ophthalmology in General Medical Education: A Scoping Review and Recommended Potential Solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: This literature review aims to explore research and conceptual pieces on the state of ophthalmology education and suggest potential ways to address current challenges. METHODS: A search was conducted in PubMed, ERIC, Web of Science, and Google Scholar with combinations of the following search terms: "ophthalmology education," "undergraduate medical education," "medical student," "United States," and "Canada." Eliminating irrelevant articles yielded 47 articles. Three were excluded because of region and focus, leaving 44. After examining the citations, we generated an additional 22 texts for review, totaling 66 articles. RESULTS: Four primary themes were identified: (1) challenges to ophthalmological education in the U.S. and Canada, (2) potential remedies for optimizing ophthalmology curriculum, (3) technology in ophthalmology education, and (4) innovative ophthalmology teaching approaches. Major challenges included the lack of a standardized curriculum and inadequate clinical exposure and skills training. A number of remedies were proposed, such as standardizing curriculum and furthering faculty involvement, utilizing technology as time-effective learning aids, and employing innovative teaching approaches such as service learning. CONCLUSION: In light of challenges in ophthalmology education, curriculum designers should consider Cognitive Load Theory (CLT) to assist students to remember meaningful exposures to ophthalmology knowledge and techniques. Based on CLT, we suggest two potential approaches to incorporating ophthalmology curriculum. The first is to embrace interdisciplinary collaborations and place ophthalmology knowledge in varied contexts to facilitate schema construction. The second is to incorporate ophthalmology diagnostics requirements into OSCEs and utilize simulation models for students to gradually increase the fidelity of tasks and devote cognitive resources fully to learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it