The Relationship Between Psychosocial State and Exercise Behavior of Older Women 2 Months After Hip Fracture
Bibliographic record
Abstract
Despite the potential benefits associated with exercise after hip fracture, those who have sustained hip fractures are among the least likely to engage in regular exercise (resistive or aerobic). This article describes the psychosocial state, specifically the self-efficacy expectations and outcome expectations related to exercise, mood, fear of falling, pain, and health status of older women who enrolled in either of two Baltimore Hip Studies (BHS), BHS-4 and BHS-5, and to test a self-efficacy-based model to explain exercise behavior after hip fracture. A total of 389 older women with hip fractures participated in these studies. The participants reported moderate confidence in their ability to exercise and a general belief in the benefits of exercise, high perceived health status, limited depressive symptoms, and some pain and fear of falling. Consistently across these two samples, age and mental status or depressive symptoms influenced outcome expectations, such that older women with more depressive symptoms or lower mental health status had weaker outcome expectations for exercise. Self-efficacy expectations consistently influenced exercise behavior across both samples. It was also consistent across both models that age, cognitive status, physical and mental health status, pain, fear, outcome expectations, and depressive symptoms did not directly influence exercise behavior.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".