Smart Grab Bars: A Potential Initiative to Encourage Bath Grab Bar Use in Community Dwelling Older Adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grab bars are often prescribed to ensure safe and independent bathing and toileting. Studies have shown that seniors do not always use grab bars when they are present or are reluctant to install them due to the associated stigma. This study sought to determine if artificial intelligence could increase grab bar use by seniors and to determine the efficacy of different cues (auditory, visual, and audiovisual combination) on the frequency of use of a grab bar. Sixty-nine healthy participants aged 60 to 86 years (average 68.7 years) were randomly assigned to three subgroups. Each subgroup tested two different cueing conditions: the no cue and one of three cued conditions (visual, auditory, or combined audio-visual). Results suggest that the smart grab increased seniors' grab bars use by 39% and that the effect was maintained after removal of the cues. Participants preferred the visual cue but the auditory cue was the most powerful. Results suggest that artificial intelligence may be an interesting avenue to increase grab bar use in community-dwelling older adults and in people requiring supervision to use grab bars on a regular basis to decrease the risk of falls during bathing or bathtub transfers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it