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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".