The Experience and Perception of Physiotherapists in Nigeria re: Fall Prevention in Recurrent-Faller Older Adults
Bibliographic record
Abstract
Background: Effective fall prevention practices are essential for reducing falls among older adults. Rehabilitation professionals like physiotherapists are essential members of the fall prevention team, yet little is known about the experiences of physiotherapists practicing fall prevention in developing nations. Objective: To explore the experiences of physiotherapists in Nigeria who practice fall prevention among older adults. Method: We adopted a phenomenological approach to the traditional qualitative design in this study. We purposefully selected and conducted face-to-face interview with twelve physiotherapists who have treated at least one older adult who reported falling two or three times within last six months. Data was analyzed using thematic analysis. Results: Four themes emerged from our participants: characteristics of recurrent fallers, fall prevention practices, hindrances to fall prevention, and strategies to promote fall prevention practices. In practice, understanding the characteristics (risk factors) of older adults with a history of recurrent falls is important for effective fall prevention practices among physiotherapists. Among other characteristics, our participants believed that older adults who have patronized “traditional bone setters/healer” are at the higher risk of having multiple falls. Conclusion: This study adds to the sparse amount of literature concerning the experience of physiotherapist in fall prevention practices in the developing world. More importantly, the findings of this study will strengthen or stimulate discussion around development of fall prevention strategies specific to the developing world context.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".