Low vision assistive technology device usage and importance in daily occupations
Bibliographic record
Abstract
UNLABELLED: When selected, accepted and used appropriately, low vision assistive technology devices (ATDs) have the potential to facilitate the performance of occupations that lead to positive outcomes. OBJECTIVE: This paper identifies some low vision ATDs currently used and explores their relative importance for the performance of daily occupation from participants' perspectives. PARTICIPANTS: 17 adults (M=56 years old, SD=15.8) with low vision we0re recruited through a purposeful sampling strategy. METHODS: Through one-on-one semi-structured telephone interviews, ATD usage data, ranking of perceived importance of ATDs and verbal data were collected from the participants. RESULTS: A total of 124 devices were identified by the participants of which 104 (83.9%) were used and 20 (16.1%), mostly adaptive computer technologies, were not. 22 (21%) mainstream aids to daily living were identified (large monitor, large screen TV, DVD player) and they ranked high in terms of perceived importance by the participants for daily activities. Verbal feedback from participants supplemented this finding. CONCLUSION: Concepts related to usage and ranking of importance of ATDs for daily occupations are multi-faceted and complex(e.g. combination of devices used, multiple equal rankings, etc.). The authors suggested future research opportunities to examine these concepts through qualitative means.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".