Depression in the elderly with visual impairment and its association with quality of life
Bibliographic record
Abstract
BACKGROUND: Visual impairment is more prevalent in the elderly and depression is common in this population. Although many studies have investigated depression or quality of life (QOL) in older adults with visual impairment, few have looked at the association between these two concepts for this population. The aim of this systematized review was to describe the association between depression and QOL in older adults with visual impairment. METHODS: A search was done using multiple electronic databases for studies addressing the relationship between QOL and depression in elders with visual impairment. The concept of QOL was divided into two different approaches, ie, QOL as achievement and QOL as subjective well-being. Comparison of QOL scores between participants with and without depression (Cohen's d) and correlations between depression and QOL (Pearson's r) were examined. RESULTS: Thirteen studies reported in 18 articles were included in the review. Nearly all of the studies revealed that better QOL was moderately to strongly correlated with less severe depressive symptoms (r = 0.22-0.68 for QOL as achievement; r = 0.68 and 0.72 for QOL as subjective well-being). Effect sizes for the QOL differences between the groups with and without depression ranged from small to large (d = 0.17 to 0.95 for QOL as achievement; no data for QOL as subjective well-being). CONCLUSION: Additional studies are necessary to pinpoint further the determinants and mediators of this relationship. Considering the high prevalence rate of depression in this community and its disabling effects on QOL, interventions to prevent and treat depression are essential. More efforts are needed in clinical settings to train health care practitioners to identify depressed elders with visual impairment and provide appropriate treatment.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".