An Optometrist-Led Eye Care Program for Older Residents of Retirement Homes and Long-Term Care Facilities
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Visual impairment among older adults residing in long-term care (LTC) facilities and retirement homes is common and can have a significant adverse impact on their quality of life. Despite the burden of illness, they frequently receive inadequate eye care. We describe an optometrist-led eye care program serving this population, including a profile of participants and the program's educational role for optometry students. METHODS: An optometrist assessed residents of LTC facilities and retirement homes. Participants received their routine eye care, which included a report to the resident's family physician, through the program. A chart review was conducted for a consecutive series of patients; data were recorded on a standardized data abstraction form. RESULTS: All residents examined had at least one (average 1.8) ocular condition. Challenges presented by residents in their assessment, such as confusion and/or impaired comprehension (14.3%), refusal or poor cooperation (13.2%), and physical limitations (8.8%), were common, indicating the necessity of adapting eye assessment procedures to the needs of this population. CONCLUSION: This study supports the involvement of optometrists in the eye care for residents of retirement homes and LTC facilities, where optometrists can be an important clinical and educational resource. The program is a useful learning opportunity for optometry students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".