Exploring prescribers’ experiences of providing assistive technology to immigrants in Canada
Bibliographic record
Abstract
Introduction: Assistive technology can help individuals with disabilities compensate for limited function, increase their independence, and enhance their quality of life. However, few studies have explored the perceptions of health professionals who prescribe assistive technology to immigrants in Canada. There is some evidence to suggest that immigrants face challenges in acquiring assistive technology due to a lack of the understanding of dominant/Canadian culture, language barriers, and limited funding. Therefore, the overarching purpose of this study is to understand prescribers’ experiences with assistive technology provision to immigrants. The objectives of this study are to: (1) understand the assistive technology prescribers’ approaches to services delivery among immigrants who are unable to communicate with prescribers in their native language; (2) identify the challenges that therapists experience with the provision of assistive technology to this population; and (3) identify potential solutions to improve access, uptake, and usage of assistive technology. The study drew upon the Human Activity Assistive Technology (HAAT) model and the Unified Theory of Acceptance and Use of Technology (UTAUT) to describe the prescription process and compare the findings for discussion. Methods: In this interpretive description study, semi-structured interviews were used as the primary method of data collection. Twelve prescribers from diverse professions with expertise in assisting immigrants who could not communicate in their native language, were interviewed once for an average of 50 minutes and a total of around 10 hours. The qualitative data was analyzed following interpretive description procedures. Results: I collected data from 12 participants (Male: n=5, Female: n=7), and identified three main themes. “Aspiring to the client-centered provision of assistive technology” described how prescribers prioritized the goals and needs of their clients during the prescription process. “Struggling with linguistic, cultural, and systemic issues” included challenges faced by participants as they strove to adopt a client-centered approach. “Implementing stopgap solutions” explored the strategies employed and proposed by participants. Conclusion: This study aimed to understand the experiences of prescribers in prescribing assistive technology to immigrants. These insights could eventually help to develop strategies to improve immigrants' access to assistive technology, ultimately enhancing their independence and quality of life.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".