A systems approach to assist policy action to prevent falls among community-dwelling older people in Australia
Bibliographic record
Abstract
OBJECTIVES: While systems thinking has gained recognition as an important approach in health policy and prevention research, its application in the context of fall prevention among community-dwelling older adults has been underutilised. Here, we build on the guiding principles of the systemic lens component of the Prevention System Change Framework to assess and identify potential changes that are required to facilitate policy action in the field of falls prevention. METHODS: We conducted a desktop search to identify policy documents encompassing falls prevention among community-dwelling older adults in Australia. Documents were considered eligible if they were published in the last 10 years and were authored or endorsed by federal or state government bodies. We collaboratively examined eligible documents to gain insights into the current policy landscape in falls prevention and to illustrate opportunities for action and the potential for strengthening partnerships. RESULTS: There is no current national policy on preventing falls in older adults in Australia. While we identified eight policy documents, none focused exclusively on falls prevention, indicating that falls are currently not perceived as a public health issue that warrants a dedicated policy framework. We identified a need for a comprehensive national policy that draws upon insights from various disciplines, suggests intersectoral collaboration, addresses health inequities and involves meaningful engagement with key stakeholders. Future falls prevention policies may benefit from clear governance structures and specific targets, along with mechanisms for monitoring and evaluating outcomes. CONCLUSION: Falls prevention is a pressing public health concern that requires dedicated policy resources. Adopting a systems-oriented approach can help reduce falls and their associated burdens on individuals and the healthcare system. Acknowledging the urgency and complexity of this challenge is a first, essential step toward crafting a comprehensive national falls prevention policy.
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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 | 0.039 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".