A public health approach to fall and injury prevention among seniors in Canada
Bibliographic record
Abstract
Introduction The need to prevent falls and related injuries among seniors is a significant public health issue in Canada, and all nations where an aging demographic puts higher numbers at risk. To address this costly and complex problem, a sustained collaboration has occurred over the past 20 years among falls prevention leaders within government, the health system, academia and local communities. Methods This review summarizes key elements of a coordinated, public health approach to the prevention of falls and related injuries among those aged 65 years, with an epidemiological analysis of fall-related hospital and mortality data for persons aged 65 years and older. Review methods included a synthesis of historical records, a scan of existing programs and findings from in-depth interviews with key informants. Results Outcomes of this sustained approach include a significant reduction in fall-related hospitalization and death rates in some Canadian provinces, which is paralleled by a growth in evidence-based falls prevention programs and services for those at risk. Conclusion This presentation concludes with a summary of the key developments in the evolution of fall and related injury prevention activities over the past two decades. In particular, how the sustained, collaborative efforts have resulted in Canada emerging as an example of success in the formation of comprehensive networks for the integration of evidence-based falls prevention into health service delivery for seniors.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".