Identifying community-dwelling older adults’ vision loss during mobility assessments: A scoping review
Bibliographic record
Abstract
BACKGROUND.: Co-occurring mobility issues and vision loss are prevalent in older adults. Vision loss can cause ambulation difficulties and falls. Community-dwelling older adults frequently require mobility-aids assessment by occupational therapists. However, therapists often lack access to medical documentation on vision or training in vision assessment to ensure that clients have adequate vision for safe mobility-aid use. PURPOSE.: This study aimed to identify screening and assessment approaches to identify functional vision loss to guide mobility-aid prescription. METHOD.: A scoping review following Arksey and O'Malley's five stages was undertaken using Medline and CINAHL databases. A data-charting form was used for extraction of information about each article, including the population, vision diagnosis, and the methodology for vision screening. The data regarding vision loss and mobility of older adults were summarized for each article. FINDINGS.: Twenty-three papers were included in the study, describing screening questions and questionnaires or assessment tools to screen for vision loss in community settings. IMPLICATIONS.: The various tools identified can better prepare therapists to prescribe mobility aids appropriate for seniors' level of functional vision and to refer clients for further assessment and intervention if warranted.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".