Exploring Older Adults' Patterns and Perceptions of Exercise after Hip Fracture
Bibliographic record
Abstract
PURPOSE: To identify exercise patterns and perceived barriers, enablers, and motivators to engaging in exercise for older adults following hip fracture. METHOD: Telephone interviews were conducted with older adults (aged 62-97 y) within 1 year after hip fracture. Participants were asked about basic demographic information; level of mobility before hip fracture; current level of mobility; and barriers, enablers, and motivators to participating in exercise. RESULTS: A total of 32 older adults successfully recovering after hip fracture completed the telephone interviews. Participants reported few problems with their mobility, and all were engaging in exercise. There were few reported barriers to exercise; the most common were health-related concerns (pain, fatigue, illness, or injury). The most frequently reported enablers were intrinsic factors (determination, seeing improvements, and making exercise part of their daily routine); in particular, the most common motivator to exercise was recovery of function to improve mobility and complete daily and leisure activities. CONCLUSIONS: This study highlights the responses of a group of older adults recovering well after hip fracture. Older adults engage in exercise despite the potential limitations associated with a hip fracture. Participants' responses underscore the importance of intrinsic factors and suggest avenues for future investigation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".