PRESIDENTIAL SYMPOSIUM: THE IMPACT OF FALLS AND FEAR OF FALLING ON OLDER ADULTS’ MOBILITY: AN INTERNATIONAL PERSPECTIVE
Bibliographic record
Abstract
Falls and Fear of falling (FOF) are common in old age. Although the adverse consequences of falls and FOF were studied extensively, most studies were either cross-sectional, focused on one context, or varied widely in definitions and outcome measures used, which prevented researchers from conducting meaningful comparisons between different nations. The International Mobility in Aging Study (IMIAS) is a population-based, cohort study (2012–2016) on community-dwelling older adults in five sites: Canada (Kingston, Saint-Hyacinthe), Albania (Tirana), Colombia (Manizales) and Brazil (Natal). Drawing on longitudinal data from IMIAS and using validated and standardized tools in all study sites, presenters in this symposium will provide an overview of their recent research to improve our understanding of factors associated with falls and FOF at an old age in diverse locations, and investigate if FOF can independently lead to disability over time. Ms. Hwang, University of Hawaii, USA will share results from her study to test whether FOF is independently associated with average minutes walking per day among older adults. Dr. Auais from Queen’s University, Canada will discuss whether FOF could independently lead to functional disabilities after a 2-year period. Dr. Vafaei from the Queen’s University, Canada will explore potential mediators that could explain the longitudinal relationship between FOF and incident disability. Finally, Dr. Gomez, University of Caldas, Colombia will present his findings after testing a simple algorithm to predict falls over time and possible implications. This work has clinical and policy implications and could improve older adults’ mobility and, ultimately, their integration in local communities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".