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Record W2305189953 · doi:10.1155/2016/4683427

Burden and Depression among Caregivers of Visually Impaired Patients in a Canadian Population

2016· article· en· W2305189953 on OpenAlexaffabout
Zainab Khan, Puneet S. Braich, Karim Rahim, Jaspreet Rayat, Lin Xing, Munir Iqbal, Karim Mohamed, Sanjay Sharma, David R.P. Almeida

Bibliographic record

VenueAdvances in Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsHotel Dieu HospitalQueen's University
Fundersnot available
KeywordsDepression (economics)MedicineVisual impairmentCenter for Epidemiologic Studies Depression ScaleCaregiver burdenRehabilitationFamily caregiversGerontologyPsychiatryPhysical therapyDiseaseAnxietyDepressive symptomsDementiaInternal medicine

Abstract

fetched live from OpenAlex

Purpose/Background. This study reports the degree of burden and the proportion at risk for depression among individuals who provide care to visually impaired patients. Study Design. This is clinic-based, cross-sectional survey in a tertiary care hospital. Methods. Caregivers were considered unpaid family members for patients whose sole impairment was visual. Patients were stratified by vision in their better seeing eye into two groups: Group 1 had visual acuity between 6/18 and 6/60 and Group 2 were those who had 6/60 or worse. Burden was evaluated by the Burden Index of Caregivers and the prevalence of being at risk for depression was determined by the Center for Epidemiologic Studies Depression scale. Results. 236 caregivers of 236 patients were included. Total mean BIC scores were higher in Group 2. Female caregivers, caregivers providing greater hours of care, and caregivers of patients who have not completed vision rehabilitation programs are at higher risk for depression.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.085
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.339
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2016
Admission routes2
Has abstractyes

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