Bibliographic record
Abstract
ABSTRACT: Falls among older adults (OAs) living in retirement homes (RHs) in Canada are a major public health concern due to high morbidity and mortality as well as significant healthcare expenditures. This quality improvement (QI) initiative, conducted for the author's Doctor of Nursing Practice (DNP) project, aimed to decrease fall rates and ED transfers related to falls among OAs in six RHs across the Greater Toronto Area in Ontario, Canada through a multipart intervention with two primary goals. First, the project aimed to facilitate RH NPs' implementation of a comprehensive fall risk assessment and fall prevention strategy in their practice by incorporating the Stopping Elderly Accidents, Deaths & Injuries (STEADI) toolkit into their armamentarium. Second, it sought to enhance the knowledge of the RHs' registered practical nurses (RPNs), personal support workers (PSWs), and unregulated care providers (UCPs) in assessing fall risk and incorporating fall prevention strategies in their daily practice. By improving NP, RPN, PSW, and UCP knowledge and increasing (by 20%) RPN, PSW, and UCP use of fall prevention strategies, this QI initiative successfully reduced fall rates in the RHs by 40.4%, with no falls requiring transfer to the ED, in the postintervention period. The results of this project highlight the need for an interdisciplinary approach to fall risk reduction in RHs that includes implementation of multifactorial intervention strategies as well as effective organizational policies and procedures for maximum impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".