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
Smartphones and applications (apps) are replacing traditional assistive technology devices for people with vision impairment (PVI) to support their mobility and independence in daily life. However, training and learning support to enable PVI to use this technology to its full advantage requires further research. A better understanding of what, and how, training and learning support is currently being provided is required to inform the future development of training and best practice in the area. This study, using an interpretive descriptive qualitative approach, aimed to explore the perspectives of trainers on the current provision of smartphone training in Australia, Canada, and Singapore. Semi-structured interviews with 22 trainers, including 13 trainers with a vision impairment, discussed how training is currently conducted, the challenges, and their ideas on what would constitute a high-quality or ideal training programme. The data were analysed using thematic analysis and six themes emerged: structure and content of training; training provides hope, independence and connection; trainers’ approach and attributes influence training; informal support and other avenues for learning; challenges associated with providing training; and suggestions to improve training. Participants highlighted that smartphone training was a source of hope for PVI and that it enabled independence. The importance of responding to clients’ emotional needs, in addition to their learning needs in an individualised and graded approach, was discussed as critical to the success of training. Trainers with vision impairment who weaved their lived experience into the training sessions found this to be beneficial to their 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".