Self-Reported Ability to Walk, Run, and Lift Objects among Older Canadians
Bibliographic record
Abstract
Aims.The purpose of the study was to develop new self-report instruments to measure the ability to walk, run, and lift objects and describe the distribution of these abilities among older Canadians.Methods.Questions were developed following a focus group. We carried out an online survey among members of the Canadian Association of Retired Persons. The distribution of each ability was described and presented graphically according to age, sex, and number of health conditions. We calculated summary scores for each ability and assessed their reliability and relationships with health status and use of health services.Results.22% of the subjects reported difficulty walking 100 m, 15% were unable to run 10 m, and 50% had difficulty lifting 10 kg. Men reported higher abilities than women but differences according to age were small. Test-retest reliability ranged from 0.89 for walking to 0.88 for running and 0.81 for lifting. Scores for the three measures correlated with other measures of health status as expected.Conclusions. The study provided new data on self-reported walking, running, and lifting abilities among older Canadians. The new measures are valid, reliable, and easy to interpret. We expect these measures to be useful in clinical and research settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".