Technologies for fall prevention in the hospital setting: A scoping review
Bibliographic record
Abstract
Objective: To identify scientific evidence about the main technologies used to prevent falls in hospitalized patients.Methods: A scoping review was carried out. Studies available in English, Portuguese, or Spanish, aiming to identify technologies to reduce the risk of falls in hospital settings in the adult and elderly population, were included. No time limit was applied.Results: Thirty articles were included in the review. The countries with the highest number of studies on the subject were the United States and Brazil. The technological solutions found include mobile applications, protocols, and software. From this list, the main technological solutions were mobile applications.Conclusions: Technologies such as mobile applications offer portability and ease in transmitting information, becoming a tool to enhance the quality of healthcare practices. The use of technological solutions to provide medical care for the elderly population is promising as such tools assist in critical training and guide patients to achieve healthy living. Technology helps in interpersonal and professional relationships. Further studies exploring new solutions or technologies are needed to build upon the existing knowledge of strategies to improve healthcare quality.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".