Baby boomers' use and perception of recommended assistive technology: A systematic review
Bibliographic record
Abstract
PURPOSE: The objective of this article is to review published studies to describe issues and quality of evidence surrounding assistive technology (AT) use by the baby boomer generation. As the baby boomer generation are ageing, they represent a new era for aged health care. In terms of helping this generation maintain independence, it is expected that there will be an increased demand for AT. METHOD: A systematic literature search of Medline, CINAHL and Cochrane was undertaken. Selected studies were critically appraised using a previously validated tool. Inclusion criteria were: research related to AT use by a population which includes baby boomers; published in peer-reviewed journals and full-text English language articles. Studies were based in acute rehabilitation units in the USA and Australia. Frequency of use and patient satisfaction surveys were the main outcome measures. RESULTS: A total of 11 eligible studies were reviewed. All were cross-sectional. Many studies indicated a significant rate of AT non-use; use rates ranged from 35% to 86.5%. Numerous factors influencing use were proposed. Study quality was upper-mid range. CONCLUSIONS: Baby boomers will place more demand on AT in the future. There is a need for high-quality research to verify current findings and highlight AT issues specific to this generation.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".