Quantifying Physical Resilience in Ageing Using Measurement Instruments: A Scoping Review
Bibliographic record
Abstract
Purpose: The capacity to recover motor function with pathology or age-related decline is termed physical resilience. It is unknown what outcome domains are captured with existing measurement instruments. Thus, this scoping review aimed to identify measurement instruments for physical resilience, identify research gaps, and make recommendations for future research. Methods: Articles were included from the search when their subject matter included the term resilience in relation to the physical health of older adults. Data on physical resilience measurement instruments were extracted using the outcome domains: body function or structure, activity and participation, and societal impact. Results: The majority of the 33 included articles involved older adults with fractures, cardiac conditions, and cancer. Many measurement instruments quantified body function or structure, and some instruments captured activity and participation, and societal impact of physical resilience. Measurement instruments were pooled into 4 categories: psychological, physiological, motor function, and psychosocial scales. No studies combined all areas of measurement. Conclusions: A potential gap of a measurement instrument capturing social aspects of physical resilience was identified. Comprehensive measurement could identify which outcome domains could be targeted to foster resilience. This knowledge might be useful across many health disciplines and contribute to therapeutic decision-making and rehabilitation strategies.
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 | 0.026 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".