National Fall Prevention Workshop: stepping up pan-Canadian coordination
Bibliographic record
Abstract
About one in three Canadian seniors will experience a fall at least once each year. Such falls are the leading cause of injury-related hospitalizations among older people. Apart from causing injury, falls can result in chronic pain, reduced quality of life and, in severe cases, death. Psychological effects of a fall may cause a post-fall syndrome that includes dependence on others for daily activities, loss of autonomy, confusion, immobilization and depression. Falls and the resulting injuries often occur due to a combination of factors, including health conditions associated with aging such as vision problems, osteoporosis, dementia and symptoms of a chronic disease. They can be due to the side effects of medications, environmental hazards and risk-taking behaviours. Fall prevention initiatives and strategies are taking place in all provinces and territories and at the national level. To enhance the collaborative understanding of these initiatives, a National Fall Prevention Workshop was held at the Canadian Injury Prevention and Safety Promotion Conference in Vancouver, British Columbia, on 17 November 2011. The Workshop was co-hosted by the British Columbia Injury Research and Prevention Unit (BCIRPU) and the Public Health Agency of Canada (PHAC). Fall prevention leads from each province and territory were invited to present their most recent activities and their plans.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".