Home-based therapy and its determinants for children with cerebral palsy, exploration of parents’ and physiotherapists’ perspective, a qualitative study, Ethiopia
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore the perceptions of parents and physiotherapists regarding home-based therapy programs for children with cerebral palsy and to understand the factors affecting adherence to home-based therapy programs. MATERIALS AND METHOD: Thematic analysis method was used to identify, analyse and report findings. Twelve physiotherapists and five caregivers were purposively sampled and interviewed. RESULTS: All transcripts were coded line by line, and the codes were then organized into categories for the development of descriptive themes and the generation of analytical themes. The data analysis followed the steps of the thematic analysis process. Seven themes emerged during the analysis: Why Home-Based Therapy? Ways of Teaching, Types of the therapy, Strategies of assessing adherence, Environmental factors, Attitude and knowledge; and Family participation. Physiotherapists use home-based therapy to prevent complications and improve functioning. They use various ways of teaching, such as explaining, demonstrating, and using pictures and videos. Physiotherapists consider several factors such as severity, age, and availability of resources before they decide the type of home therapy programs. However, parent's participation was low; and strategies to monitor and evaluate adherence were also low. Low family support, limited recourse, lack of knowledge and poor attitude negatively affected adherence to home-based therapy. CONCLUSIONS: Our finding revealed that physiotherapists use quite limited methods of teaching, and do not properly monitor adherence of the home-based therapy. Additionally, family participation to select type of therapy and to set goal were low.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".