Reducing falls in inpatient older adults: Quality improvement initiative
Bibliographic record
Abstract
BackgroundFalls and fall-related injuries are common among older adults, adversely affecting their functional independence and quality of life. By 2043, one in three Canadians falls each year, resulting in 85% of hospitalizations, which cost $2 billion annually.ObjectiveThis study aimed to reduce inpatient falls among older adults with cognitive impairment in a rural Ontario hospital.MethodsA fall reduction project was implemented at the hospital to improve clinical care since January 2024. The project included fall risk screening, ensuring a fall bundle was in place, and monthly meetings with hospital staff and patient representatives to identify potential barriers and facilitators to the project. The project involved two components: 1) nurses evaluating fall risk using validated tools and 2) implementing a fall prevention bundle. Data was retrieved from the hospital's electronic medical records. The outcome of interest was the fall rate before and after the intervention.ResultsThe initiative has reduced the inpatient fall rate from 16.25 falls per 1000 bed days in 2023 to 11.33 falls per 1000 bed days in 2024. Almost 57% of people who fell were cognitively impaired.ConclusionsThe project reduced the inpatient fall rate by 30% within a year. The involvement of patients and their families in the initiative has made the project meaningful to the community. However, no change was observed in the 30-day readmission rate, prompting the research team to conclude that inpatient interventions are insufficient. Further research on collaborative care involving the pharmacy department and community partners is recommended.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".