An Intervention Program to Reduce Falls for Adult In-Patients Following Major Lower Limb Amputation
Bibliographic record
Abstract
A qualitative and quantitative assessment was conducted regarding falls sustained by in-patients receiving rehabilitation therapy following major lower limb amputation at the Glenrose Rehabilitation Hospital. During the nine-month assessment period, 18 of 58 patients in the amputee unit experienced a fall, of which 17% resulted in a moderate injury. The majority of falls occurred during patients' use of a wheelchair (14 of 18) and involved poor balance (nine of 14). Patient wheelchair self-transfers accounted for 71% (10 of 14) of the falls, while sitting in the wheelchair and reaching represented 29/ (four of 14). The hospital's rehabilitation program teaches patient safety including using assistive devices such as wheelchairs but did not include a comprehensive graded learning path to monitor patients' ongoing risk for falls. Based upon the data collected, an intervention program was initiated to improve patient safety and reduce the number of falls. The multidisciplinary program encompassed aspects ranging from an environmental assessment of the patients' room to medication management, continuous patient wheelchair skills training and alteration of the care plan. The effectiveness of the intervention program was assessed through a series of interviews and questionnaires administered to medical personnel. This article presents the preliminary data collected during the first three months of the six-month study. Overall, satisfaction has significantly improved as a direct result of the intervention program. The article provides evidence-based interventions that improve safety for a subset of in-patients known to be susceptible to falls when using wheelchairs. Other in-patient groups will also benefit from these findings as many are universally applicable.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".