Preventing Falls
Bibliographic record
Abstract
Abstract A fall is the mechanism of injury for most fragility fractures. Falls are preventable and should not be viewed as an inevitable consequence of the ageing process. Falls and fragility fractures frequently result in both short- and long-term disability and can be life-changing with considerable deterioration in health-related quality of life, increased dependency and social isolation. The causes of falls are individual and multifactorial. Risk factors interact dynamically and can be broadly classified into three main categories: demographic, intrinsic and extrinsic. Frailty, sarcopenia, falls and fragility fractures are linked and should be identified and receive proper intervention. Evidence-based processes and tools for interdisciplinary screening, assessment and management of risk of falling are available and can guide healthcare professionals. Involving patients and their families is essential in developing and implementing a person-centred fall prevention care plan. Nurses are central to fall prevention strategies in both hospital and community settings, working collaboratively with the entire interdisciplinary team, but most often with physiotherapists. The aim of this chapter is to explore the role of the practitioner working in acute hospital units, ambulatory care/outpatient clinics and community/home care settings and with people transitioning from hospital to home in preventing further falls in older people who have sustained a fragility fracture.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.025 |
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".