Using Patient Simulation to Promote Best Practices in Fall Prevention and Postfall Assessment in Nursing Homes
Bibliographic record
Abstract
BACKGROUND: Fall-related injuries rise with age and are of particular concern for frail populations living in nursing homes. LOCAL PROBLEM: The Perley and Rideau Veterans' Health Centre is a large nursing home in Ontario, Canada. In 2019, we conducted internal audits of our Falls Prevention Program and identified notable variations in staff's response to a resident fall. INTERVENTIONS: We developed an in situ patient simulation program of a resident fall. METHODS: This was a mixed-methods evaluation of participants' perspectives of a simulation-based interprofessional education program for fall prevention. RESULTS: Participants indicated high-level support for simulation-based learning, with more than 80% of the participants expressing that they will apply these skills in the future when caring for a resident who falls. CONCLUSIONS: Our findings indicate that simulation-based training is well received by frontline workers in a nursing home setting and can be conducted as part of a typical shift with minimal disruption to resident care.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".