Effect of an educational rehabilitation program on prevention of falls after stroke
Bibliographic record
Abstract
Objective: Fall is common in patient with stroke. The aim of the study was to evaluate the effect of an educational rehabilitation program on prevention of falls after stroke.Methods: Quasi-experimental design was utilized. Setting: Neurology Department and Outpatient Clinics at Neurology, Psychiatry and Neurosurgery Hospital at Assiut University. Sample: Sixty adult patients diagnosed with stroke. Patients were equally divided into two equal groups (study and control) 30 patients each. Tools: Tool I-Patient assessment sheet. Tool II-Morse Fall Scale.Results: A statistically significant improvement of circumstances and consequences of falls and decreasing the injuries due to fall (p < .01) among the study group in comparison to the control group ones where, study group showed a decrease in the number of falling episodes (2.07 ± 0.78 vs. 5.4 ± 1.73), an improve in their ability to get up independent (83% vs. 23.3%), no need for medical attention (3.3% vs. 66.7%), and no restriction to their activities after falling (0.0% vs. 56.7%).Conclusions: The educational rehabilitation program had a statistically significant effect on the improvement of circumstances and consequences of falls and decreasing the injuries due to fall among the study group than among the control group. Recommendation: Simple illustrated educational booklets should be available for stroke patients. Replication of this study on a larger sample with extending the follow-up period to 6 months is suggested.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".