Follow-up in low vision rehabilitation for users of assistive technology: a scoping review
Bibliographic record
Abstract
Assistive technology (AT) is crucial for aiding activities of daily living in individuals with visual impairment; yet, without systematic follow-up device abandonment rates remain high. This scoping review synthesizes existing literature on follow-up processes in individuals with visual impairment undergoing vision rehabilitation with AT. Employing the Arksey and O’Malley framework, this review comprehensively searched seven databases, identifying 1,061 articles, of which 43 were selected for analysis, using the concepts of visual impairment, rehabilitation, and assistive technology. The publications span from 1989 to 2022. Most studies (n = 36, 83%) utilized a mixed-methods design, and 51% (n = 22) originated from the United States. Devices for near vision were the most commonly prescribed type of AT. Follow-up methods included questionnaires and interviews, with most follow-ups conducted at the client’s home. Follow-up timing varied across studies, whereby 37% (n = 16) occurred after one or more years and 33% (n = 14) between one week and four months. Three categories of outcome measures emerged: generic outcomes, task-specific outcomes, and a combination of both. The review identified several gaps in the literature, including a scarcity of research concerning follow-up of AT particularly for both the type and timing of follow-up.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".