Users' Perceptions of the Impact of Electronic Aids to Daily Living Throughout the Acquisition Process
Bibliographic record
Abstract
This study investigated the experience of seven new users of a particular type of assistive technology through the stages of anticipating, acquiring, and using an electronic aid to daily living. A mixed methods research approach was used to explore each of these stages. The Psychosocial Impact of Assistive Devices Scale was used to measure the perceived impact of the new assistive technology on users' quality of life, and findings were further explored and developed through open-ended questioning of the participants. Results indicated that preacquisition of the device, users predicted that the electronic aid to daily living would have a positive impact on their feelings of competence and confidence and that the device would enable them in a positive way. One month after acquiring the device a reduced, yet still positive, impact was observed. By 3 and 6 months after acquisition, perceived impact returned to the same positive high level as preacquisition. It is suggested that prior to receiving the device, potential users have positive expectations for the device that are not based in experience. At the early acquisition time, users adjust expectations of the role of the assistive technology in their lives and strive to balance expectations with reality. Three to 6 months after acquiring an electronic aid to daily living, the participants have a high positive view of how the device impacts on their lives based in experience and reality. A model illustrating the electronic aids to daily living acquisition process is proposed, and suggestions for future study are provided.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".