Mobility and Cognition in Seniors. Report from the 2008 Institute of Aging (CIHR) Mobility and Cognition Workshop
Bibliographic record
Abstract
BACKGROUND: The annual Scientific Meeting of the Canadian Association on Gerontology was held on October 24 and 25, 2008 in London, Ontario. Prior to the annual meeting, mobility and cognition experts met on October 23, 2008 to engage in a pre-conference workshop. METHODS: Discussions during the workshop addressed novel areas of research and knowledge and research gaps pertaining to the interaction between mobility and cognition in seniors. RESULTS: Workshop presenters moved from the neuromuscular, biomechanics, and neurology of gait impairments, and falls through the role of cognition and mood on mobility regulation to the whole person in the environment. Research gaps were identified. CONCLUSIONS: Despite a consensus that mobility and cognition are increasingly correlated as people age, several gaps in our understanding of mechanisms and how to assess the interaction were recognized. The gaps originally identified in 2008 are still pertinent today. Common and standardized assessments for "mobility and cognition" are still not in place in current practice. Interventions that target mobility and cognitive decline as a single entity are still lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".