Factors That Influence the Occupational Engagement of Older Adults with Low Vision: A Scoping Review
Bibliographic record
Abstract
Introduction: Prior research has provided occupational therapists with an understanding of the negative impact of low vision on self care, leisure and productivity. In order to guide future low-vision rehabilitation services, an understanding of the factors that influence the occupational engagement of older adults with age-related vision loss (ARVL) is also needed. Method: A scoping review of the literature was conducted in order to identify those factors that have been shown to influence the occupational engagement of older adults with ARVL, and to identify future research needs. Findings: As identified in this scoping review, five types of factors were shown to influence occupational engagement for older adults with ARVL including: demographic variables, emotional components, behavioural components, diagnostic components, and environmental aspects. Conclusion: Although findings pertaining to personal factors can inform practice, few studies explored the influence of environmental factors on occupational engagement. Given that occupation is a result of person-environment transactions, it is important that future research more fully explores environmental influences in order to enable occupational therapists to deliver services that optimize the occupational performance of seniors with ARVL.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".