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Record W1966775823 · doi:10.5770/cgj.17.116

The Perceived Needs and Availability of Eye Care Services for Older Adults in Long-term Care Facilities

2014· article· en· W1966775823 on OpenAlexaffvenueabout
Hélène Kergoat, Hélène Boisjoly, Ellen E. Freeman, Johanne Monette, Sylvie Roy, Marie‐Jeanne Kergoat

Bibliographic record

VenueCanadian Geriatrics Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsJewish General HospitalMontreal General HospitalHôpital Maisonneuve-RosemontUniversité de Montréal
Fundersnot available
KeywordsMedicineEye careLong-term careGerontologyFamily medicineHealth careVisual impairmentNursingOptometryPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The objective was to evaluate the eye care services offered to older residents living in long-term care facilities (LTCFs). METHODS: A questionnaire targeting residents aged ≥65 years was sent to all LTCFs in Quebec. Questions related to the institution's characteristics, demographic data related to residents, oculovisual health of residents and barriers to eye care, eye care services offered within and outside the institution, and degree of satisfaction regarding the eye care services offered to residents. RESULTS: 196/428 (45.8%) LTCFs completed the questionnaire. Participating LTCFs had an average of 97.0 ± 5.1 residents with a mean age of 82.8 ± 3.0 yrs and 69% women. Eye care services were mostly offered outside the institution, on a "per request" basis. The main barriers to eye care were the perception that residents could not cooperate and the lack of eye care professionals. Most LTCFs were satisfied with the eye care services offered to residents. CONCLUSIONS: The fact that the LTCFs were satisfied with the eye care services offered to their residents, although it was neither provided on a regular basis nor to all residents, suggests that eye care professionals should take a proactive educational role for improving services to older institutionalized adults.

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 score0.038
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.284
Teacher spread0.273 · 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

Citations16
Published2014
Admission routes3
Has abstractyes

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