Do Home Adaptations Prevent Falls for Older People?
Bibliographic record
Abstract
IntroductionAround a third of people aged 65+, and around half of people aged 80+ fall at least once a year in the United Kingdom. Homes may be adapted to try to prevent falls, but evidence on the effectiveness of home adaptations is limited. Objectives and ApproachOur objective was to determine if proactive (Care&Repair service) and reactive (rapid response) home adaptations provided by Care&Repair Cymru resulted in a reduced risk of a fall for older people aged 60+ in Wales. We constructed a longitudinal dataset from the Secure Anonymised Information Linkage Databank. We created quarterly intervals for 5-years pre and post the date a home adaptation was received, or a randomly assigned date for the comparator. Per quarter, we created a binary indicator of whether someone had a fall at home that was recorded in either the emergency department or hospital admission data sources for Wales. We included key demographic variables as covariates. We analysed the data using logistic regression and a difference-in-difference approach. ResultsWe analysed 634,046 individuals, of whom 60,794 received the proactive Care&Repair service, and 47,244 received the reactive rapid response service. People receiving proactive or reactive home adaptations from Care & Repair were at around a two-fold increased risk of falling, compared to those who did not receive home adaptation with Odds Ratios (ORs) of 2.13 [95%CI: 2.07, 2.20] and 2.73 [2.64,2.81] respectively. Falls risk increased per quarter for all individuals, but after intervention delivery, the rate of increase fell in the intervention groups. ConclusionPeople receiving home adaptations from Care&Repair had a higher chance of a fall, indicating the service was successfully identifying those in need. Falls risk increased for everyone over time, but this was counteracted for people receiving an intervention.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".