A programme of support for care assistants of children admitted with cerebral palsy
Bibliographic record
Abstract
Background: Cerebral palsy affects children's movement and posture because of damage to the brain's development. Care assistants in healthcare facilities provide care to children. Caring for the children is overwhelming, hence support is required. Such support is absent, causing frustration among care assistants, which leads to poor quality care for children. Objectives: To explore and describe the experiences of care assistants of children admitted with cerebral palsy in healthcare facilities of the Gauteng province, and to develop a support programme for care assistants. Method: A qualitative, exploratory, descriptive, and contextual research design was used. Participants were selected from healthcare facilities in Gauteng province. Semi-structured interviews were used to collect data. Content data analysis was used to analyse data. The results were used to develop a support programme for care assistants. Results: Three themes emerged, namely, a lack of training opportunities, a lack of resources, and a lack of support. The results were used to develop a support programme, using the three steps of the Donabedian model for care: structure, process and outcome. Conclusion: Care assistants are not given training opportunities, work with limited resources and are not supported, hence the development of a support programme. If effectively utilised, the programme can lead to staff satisfaction and improvement of quality care for children. Contribution: The study enabled managers in healthcare facilities to see the need for policy and the need for support strategies for care assistants. A support programme was further developed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".