COMMUNITY-ENGAGED AND POLICY RELEVANT FALLS PREVENTION RESEARCH IN CANADA AND INDIA
Bibliographic record
Abstract
Falls among seniors are a global issue. While it has been estimated that 70% of the world’s older adults are and will be in low and middle income countries (LMIC), most of the research in the area of falls emerge from high income countries (HIC). This presentation will highlight two examples from community-based falls research projects in Canada (intervention study) and in India (epidemiological study). The Canadian example will highlight an integrated partnership which enabled a falls prevention intervention to be embedded within the health system infrastructure to reach older home care clients. The Indian example will highlight the data gaps and an epidemiological study on functional capacity and falls in seniors. The implications of these two examples for the implementation of research into practice and policy, uptake among seniors, sustainability of intervention after the research phase, and scope for global engagement and partnership for translational research will be discussed.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".