Assistive Technology Needs and Measurement of the Psychosocial Impact of Assistive Technologies for Independent Living of Older Hispanics: Lessons Learned
Bibliographic record
Abstract
(1) Knowledge about the assistive technology (AT) needs and psychosocial impact of AT in different populations is needed because the adoption, retention, or abandonment of AT may be influenced by the psychosocial impact that AT has on its users. The aims of this study were to: (a) identify the AT needs of a sample of Hispanic older adults with functional limitations, (b) describe the psychosocial impact of these technologies on the sample's quality of life, and (c) describe the methodological challenges in using the Puerto Rican version of the Psychosocial Impact of Assistive Device Scale (PR-PIADS) with a Hispanic sample. (2) Methods: This study used a cross-sectional design conducted with a sample of 60 participants. Data was collected using the Assistive Technology Card Assessment Questionnaire (ATCAQ) and the PR-PIADS. Data analyses included descriptive statistics and bivariate analysis. (3) Results: The sample's most frequently reported needs for AT devices were in the areas of cooking, home tasks, and home safety activities. The sample reported a positive impact of AT use in their quality of life. Several methodological challenges of the PIADS were identified. (4) Conclusions: The sample has unmet needs for using AT devices to overcome difficulties in daily living activities.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".