Examining Fall Risk Assessment in Geriatric Rehabilitation Settings Using Translational Research
Bibliographic record
Abstract
PURPOSE: The objective of this study was to identify gaps in and to improve the falls prevention strategy (FPS) of an inpatient rehabilitation facility (IRF) in Toronto, Canada. DESIGN: A modified version of the Stanford Biodesign Methodology was used. METHODS: Chart reviews, a focus group (n = 8), and semistructured interviews (n = 8) were conducted to evaluate the FPS. FINDINGS: Admission Functional Independence Measure score, age, and gender significantly correlated with risk for a fall. The tool used at this IRF was not effectively capturing patients who were at high risk for falls. All healthcare providers interviewed were knowledgeable of fall risks; however, a patient's fall risk status was rarely discussed as a team. CONCLUSIONS: The findings informed recommendations to improve the overall FPS at this IRF. CLINICAL RELEVANCE: Staff may require more coaching for implementing preventative measures/ensuring accountability and evaluating whether current strategies work. These insights can guide improvement initiatives at similar facilities elsewhere.
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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 | 0.037 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".