Topical Review: Teaching Serious News Delivery in Eye Care
Bibliographic record
Abstract
SIGNIFICANCE: Eye doctors regularly convey serious illness news to their patients. There is an evolving understanding of how medical educators can effectively teach this vital communication skill during real-time patient care. This article proposes teaching strategies to improve clinical optometric education related to serious illness conversations.Effectively conveying serious illness news is an essential skill in optometry practice. Established protocols can help optometrists navigate these nuanced, emotional, and complex conversations with patients, yet protocols for teaching this skill in eye care settings have not been described. Clinical educators need discrete strategies for making such pivotal communication skills learnable in an environment where patient care, teaching priorities, and limited resources are regularly juggled. Despite the importance of this competency, limited study has focused on teaching optometry learners to deliver serious eye news. In this article, we explore the importance of optometry talk, serious news delivery tools, and considerations for optometric educators teaching serious news delivery. We then adapt specific strategies from medical education to help optometry educators teach serious news delivery in clinical settings.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".