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Record W2086845666 · doi:10.5770/cgj.v14i1.3

An Optometrist-Led Eye Care Program for Older Residents of Retirement Homes and Long-Term Care Facilities

2011· article· en· W2086845666 on OpenAlexaffvenue
Tammy Labreche, Paul Stolee, Jordache McLeod

Bibliographic record

VenueCanadian Geriatrics Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineLong-term careOptometryPopulationEye careFamily medicineMedical emergencyGerontologyNursingEnvironmental health

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.362
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2011
Admission routes2
Has abstractyes

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