Falls among community-dwelling elderly people in selected districts of Umutara province, Republic of Rwanda: the role of the physiotherapist
Bibliographic record
Abstract
Introduction Falls among elderly people have been identified as a significant and serious medical problem confronting a growing number of older people. Aim The purpose of the study was to identify the risk factors for falls among the community-dwelling elders in the Umutara Province of Rwanda. Method A cross-sectional convenience survey using a self-administered questionnaire was used, with a sample of 200 elders, both male and female , aged 60 and older. Data was analyzed using SPSS. Chi-squares and Fisher's exact tests were used to test associations between variables. Results Nearly a quarter (23.2%) of the community-dwelling elderly people had multiple falls in the previous year. Risk factors significantly associated with increased falling in the elderly included advanced age, gender, joint stiffness and lower extremity muscle weakness. Loss of balance and coordination, vision deficits , painful joints, multiple drug use and prolonged use of sedatives, and antidepressants were also potential risks of falling. Men fell more often than women. Men tended to suffer outdoor falls, while women were likely to sustain indoor falls. Injury rates were also high: hip, lower back and ankle injuries were the most prevalent. Conclusion Potential risk factors for falls include characteristics such as: physiological changes (age), chronic illnesses, chronic medication and multiple drugs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".