Finding balance: using collaboration and evidence to help prevent seniors' falls
Bibliographic record
Abstract
Background An estimated one in three persons over the age of 65 is likely to fall at least once a year. In 2008 older adults' falls were the leading cause of injury hospital admissions and injury emergency department visits in Alberta, Canada. From 1999 to 2008 there has been a 30% increase in the number of older adults admitted to hospital due to a fall and a 54% increase in the number of older adults seen in an emergency department due to a fall. Aims/Obejectives/Purpose The purpose of this initiative was to create greater awareness about seniors' falls and promote targeted, evidence-based falls risk prevention messages. It also aimed to connect seniors, families and health care providers to programmes in their communities. Methods A communications strategy using various tactics, from social media to print advertisements, was developed to promote proven seniors' falls prevention interventions. Tools and resources were also promoted among practitioners. Results/Outcome Since 2008 when the initiative started we have seen a 13% increase in seniors reporting they are taking actions to prevent falling. An additional 7% of seniors are ‘keeping active’ and a further 9% are ‘watching their step,’ two key messages of the strategy. Significance/Contribution to the Field Falls prevention among seniors is pivotal to reducing the burden of injury on Albertans. Providing knowledge, tools and support to community stakeholders within a variety of disciplines is a viable method to address falls among seniors, perhaps influencing practice in other injury areas.
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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 | 0.069 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".