Predictors of Bath Grab-Bar Use among Community-Living Older Adults
Bibliographic record
Abstract
ABSTRACT Bathrooms are a common location for falls among older adults. Bath grab bars can assist in promoting safe bath transfers. The aim of this study was to identify predictors of bathroom safety-device use among community-living seniors. A two-stage sampling strategy was used to select, first, a random sample of non-universal apartment buildings and a matched sample of universal buildings, from among non-profit apartment buildings in two Canadian regions; and second, a random sample of participants within each building. A total of 550 seniors participated in face-to-face interviews in their apartments. Participants within each building type presented with similar profiles. A logistic regression was used to identify predictors of grab-bar use among participants who had grab bars and entered the bathtub on a regular basis (n= 478). Significant predictors, in order of odds ratios, were bathing difficulties, ease of grab-bar use, living in buildings with policies supporting universal access to grab bars, having a history of falls, and reporting few psychosocial consequences of grab-bar use. Findings of this study emphasize the importance of promoting access as a key strategy for increasing use and have important implications for policy planning and falls-prevention initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".