Improving Policy for the Prevention of Falls Among Community-Dwelling Older People—A Scoping Review and Quality Assessment of International National and State Level Public Policies
Bibliographic record
Abstract
Objectives: Effective public policy to prevent falls among independent community-dwelling older adults is needed to address this global public health issue. This paper aimed to identify gaps and opportunities for improvement of future policies to increase their likelihood of success. Methods: A systematic scoping review was conducted to identify policies published between 2005–2020. Policy quality was assessed using a novel framework and content criteria adapted from the World Health Organization’s guideline for Developing policies to prevent injuries and violence and the New Zealand Government’s Policy Quality Framework. Results: A total of 107 articles were identified from 14 countries. Content evaluation of 25 policies revealed that only 54% of policies met the WHO criteria, and only 59% of policies met the NZ criteria. Areas for improvement included quantified objectives, prioritised interventions, budget, ministerial approval, and monitoring and evaluation. Conclusion: The findings suggest deficiencies in a substantial number of policies may contribute to a disconnect between policy intent and implementation. A clear and evidence-based model falls prevention policy is warranted to enhance future government efforts to reduce the global burden of falls.
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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 | 0.146 | 0.286 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".