Occupational Therapists’ Perceptions about the Non-Use of Recommended Assistive Technology (AT)
Bibliographic record
Abstract
First this chapter will present an overview about assistive technology (AT). Next factors identified in the literature and thought to influence AT use will be presented. The second part of the chapter will present the findings extracted from a larger three-phase study, which aimed to obtain occupational therapists’ perceptions about AT non-use. Drawing from their experiences therapists identified the client-, AT-, intervention-focused factors which they had found to influence use. Some of these factors were similar to those identified in the literature, with therapists offering additional perspectives about the role these issues actually played in affecting AT use. In addition, therapists highlighted other important factors, not previously identified as influential or which had received little attention. Overall results showed support for a client-centred approach during the provision of AT. Suggestions were made to incorporate the Matching Person with Technology (MPT) model into current practice to guide the AT provision process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".