Abstracts from the 42nd Annual Scientific Meeting of the Canadian Geriatrics Society
Bibliographic record
Abstract
Background/Purpose: Fall-related injuries can reduce older adults' independence and result in personal and economic burden.The type and use of assistive technologies as well as home modifications likely influences fall reduction and injury prevention.This effect is poorly understood.We sought to detail the contributions of assistive technology and in-home modification on falls, fall frequency, fall severity and fall location within the homes of community-dwelling older adults through a systematic review.Method: From 3 databases (Medline; CINAHL; Web of Science Core Collection) 3920 articles were sourced and assessed using inclusion and exclusion criteria.The outcome variables of interest were fall frequency, fall location, injury, mortality, and hospitalization.Two independent reviewers screened each study and completed data extraction.Reporting is in accordance with PRISMA 2020.Results: Twenty-two studies met the criteria.The most frequent assistive technologies and home modifications reported were canes (n=4), walkers (n=4), handrails (n=9), and grab bars (n=15).Their influence on falls depends on a variety of factors including fall history, history of assistive device use, and whether the device was present at the time of the fall.Discussion: Our findings may provide a basis for more intentional prescription of ambulatory assistive technologies and evidence-based recommendations of home modifications to prevent falls among community-dwelling older adults. Conclusion:This systematic review provides an understanding of how fall-related outcomes vary with the use of assistive technologies and home modifications in different areas of the home of community-dwelling older adults.Study protocol registration (PROSPERO ID: CRD42022370172).
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.021 |
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".