Health-related and subjective quality of life of older adults with visual impairment
Bibliographic record
Abstract
PURPOSE: To document health-related quality of life (HRQOL) and subjective quality of life (SQOL) and explore their correlates in older adults seeking services for visual impairment (VI). METHOD: A convenience sample of 64 participants (79.3 +/- 5.9 years) with VI was interviewed at home. HRQOL was measured with the Visual Function Questionnaire-25 and SQOL with the Quality of Life Index. The potential correlates were as follows: personal factors (sociodemographic characteristics, co-morbidity, depressive symptoms, activity level), environmental factors (technical aids, social support) and participation in daily activities and social roles (level and satisfaction). RESULTS: Compared to normative data from previous studies of older adults, the participants had lower HRQOL but similar SQOL. Greater level of participation in social roles, higher perceived activity level, use of a writing aid and greater satisfaction with participation in social roles together explained better HRQOL (R2 = 0.66). Fewer depressive symptoms, greater satisfaction with participation in social roles and with social support and fewer co-morbidities together explained better SQOL (R2 = 0.70). CONCLUSIONS: HRQOL of older adults with VI is mainly explained by level of participation correlates, while their SQOL is mainly explained by depressive symptoms and satisfaction variables. The results also underscore the importance of social roles for HRQOL and SQOL of this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".