Sounding the Alarm to Healthcare Leadership to Establish a Standardized Evidenced-based Falls Prevention Program: An Integrative Literature Review
Bibliographic record
Abstract
The dilemma for healthcare leadership is that interventions to prevent patient falls exist, but over the years it has been unclear as to which ones are the most effective and what strategies should be implemented to best support their needs (Delaforce et al., 2023). Because of the fact that national initiatives are aimed at preventing patient harm from falls, healthcare leaders have come to the conclusion that in order to be effective, a falls prevention program needs to be multi-faceted, which in turn produces a complex system. However, as the system becomes more complex, the risk of failures towards implementation also increases because the implementation of a falls prevention program can be influenced by several factors. Factors such as, environmental and contextual issues; staff knowledge, beliefs and attitudes; organizational culture and climate; staff workloads; patient education; and access to appropriate equipment to name a few, which are all driven by healthcare leadership (Ayton et. al, 2017). For this reason, the purpose of this study was to sound the alarm to healthcare leadership to establish a standardized evidenced based falls prevention program. By focusing on this, the researcher was successful in highlighting a series of fall risk assessment tools and interventions that has been known to develop fall prevention programs within healthcare. Equally important, the researcher provided several themes that has known to both inhibit and build fall prevention programs. Thereafter, the researcher then suggested two leadership strategies, reflexivity and resonant, for healthcare leaders to consider adopting as a means to help them develop effective fall prevention programs going forward.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".