Training and learning support to use smartphones and apps for people with vision impairment (PVI): A multi-site qualitative study on trainers' perspectives from Australia, Canada and Singapore
Bibliographic record
Abstract
The study explores how smartphones and applications are replacing traditional assistive technology devices for people with vision impairment (PVI), supporting their mobility and independence. It highlights the importance of training and learning support for PVI to fully utilize this technology, identifying it as an area needing further research. Using an interpretive descriptive qualitative approach, the study examines trainers' perspectives on smartphone training in Australia, Canada, and Singapore through semi-structured interviews with 22 trainers, including 13 with vision impairment.Thematic analysis of the data revealed six key themes: the structure and content of training, the hope, independence, and connection training provides, the influence of trainers' approach and attributes, informal support and other learning avenues, challenges in providing training, and suggestions for improvement. Participants emphasized that smartphone training offers hope and independence to PVI, and stressed the importance of addressing clients' emotional and learning needs through an individualized and graded approach. Trainers with vision impairment who incorporated their lived experiences into training found it beneficial for clients' learning and adjustment to vision loss.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".