Poststroke Fear of Falling in the Hospital Setting
Bibliographic record
Abstract
PURPOSE: Fear of falling (FoF) has a negative impact on older adults, however there is a paucity of research regarding the development and impact of FoF after stroke. Therefore, our objectives were to determine the proportion of individuals with FoF and the affect of FoF during the immediate poststroke period. METHODS: This observational study of baseline data from a pilot cohort study includes a convenience sample of 28 adults with acute stroke before discharge home. Measures include self-reported FoF, the Falls Efficacy Scale-Swedish Version [FES(S)], Stroke-Specific Quality of Life (SS-QOL), performance and satisfaction with performance (Canadian Occupational Performance Measure), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9). RESULTS: Fifteen (54%) of the participants reported FoF. Those with FoF were more likely to have decreased SS-QOL domain scores for energy (p = .013), personality (p = .015), and thinking (p = .008); decreased performance (self-care, productivity, and leisure) (p = .019) and satisfaction with performance (p = .010); and increased anxiety (p = .002) than those without FoF. CONCLUSIONS: Those with FoF demonstrated significantly increased anxiety and showed decreased performance and satisfaction with performance, energy, thinking, and personality than those without FoF. This suggests that poststroke FoF is related not only to physical challenges but also to cognitive and emotional factors in the poststroke period. Identifying and treating these conditions should be evaluated as a means to decrease FoF and improve outcomes post stroke.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".