Current Practice of Physiotherapists in the Management of Children With Cerebral Palsy in Benin Country: A Cross‐Sectional Online Survey
Bibliographic record
Abstract
BACKGROUND: In low- and middle-income countries, physiotherapy is often the only available rehabilitative care, making the quality of physiotherapy care particularly important. However, how well physiotherapy practice in managing children with cerebral palsy (CP) is supported by evidence-based guidelines remains unclear. This study aims to describe the current practice of physiotherapists in managing children with CP in Benin, a French-speaking country in Sub-Saharan Africa. METHODS: This study used a cross-sectional online survey, addressed to physiotherapists working in Benin. RESULTS: A total of 91 physiotherapists participated in the study, 52 of whom worked with children with CP. One-third of the children with CP treated were under 2 years old. Physiotherapy sessions were delivered twice or thrice weekly and lasted 30-45 min. Most of the physiotherapists working with children with CP (63.5%) received no certified paediatric neurorehabilitation training. About 54% rarely or never used a standardized outcome measure, and 36.5% used such measures occasionally. CP-specific common classification systems were mostly unused and unknown. The most common intervention modalities were handling techniques (muscle stretching, joint mobilization and Bobath therapy [for movement normalization]), but functional exercises (milestones) were also commonly used. About 58% felt confident in the management of children with CP, and the need for training on modern approaches was stressed. CONCLUSION: The management of children with CP in Benin does not meet the standards of evidence-based practice. The results of this study will help develop strategies to improve the physiotherapy quality of care provided to children with CP in Benin and beyond.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".