Successful Aging Through Successful Accommodation With Assistive Devices
Bibliographic record
Abstract
OBJECTIVES: To provide a profile of older adults who successfully accommodate declines in capacity by using assistive devices. METHOD: Using the National Health and Aging Trends Study, we provide national estimates of prevalent, incident, and persistent successful accommodation of mobility and self-care activity limitations. For incident and persistent accommodation groups, we describe their subjective wellbeing and participation restrictions, health and functioning, demographic and socioeconomic characteristics, and acquisition of assistive devices and environmental features. We estimate regression models predicting incident and persistent successful accommodation and the extent of wellbeing and participation restrictions for incident and persistent groups (vs. those who are fully able). RESULTS: Nearly one-quarter of older adults have put in place accommodations that allow them to carry out daily activities with no assistance or difficulty. In adjusted models, incident and persistent successful accommodation is more common for those ages 80-89, those with more children, and those living in homes with environmental features already installed; wellbeing levels for these groups are similar and participation restrictions only slightly below those who are fully able. DISCUSSION: A focus on facilitating successful accommodation among those who experience declines in capacity may be an effective means of promoting participation and wellbeing in later 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".