EXPLORING THERAPY-RELATED CONSTRAINTS IN THE DELIVERY OF SPEECH AND LANGUAGE THERAPY SERVICES TO CHILDREN WITH CEREBRAL PALSY IN KIBERA SLUMS, NAIROBI CITY COUNTY, KENYA
Bibliographic record
Abstract
This study sought to establish therapy-related constraints in delivering Speech and Language Therapy (SLT) services to children with CP in Kibera slums. The study was guided by the four-level model of the healthcare system and adopted a descriptive survey research design. The target population included caregivers of children with CP, speech therapists (in training), occupational therapists, and other healthcare providers offering speech services, a total of 51 participants. Purposive sampling was used to select study participants, and the entire study population served as the sample size. Data was collected through questionnaires for caregivers and interviews with speech therapists (in training), occupational therapists, and other healthcare providers offering speech services. The study ensured validity through professional reviews and a pilot study conducted at a similar institution within the Lang’ata constituency in Nairobi County. Reliability was determined using Cronbach’s Alpha to assess the consistency of research instruments. Qualitative data from interviews were analyzed thematically, while quantitative data from the questionnaires were analyzed using descriptive statistics, including frequency counts, percentages, and tables utilizing SPSS. The study revealed that a significant percentage of caregivers were discontented with the way the therapist treated their children with cerebral palsy, which not only resulted to inconsistent therapy attendance, but also poor cooperation with the speech therapists. In addition, inadequacy of available speech therapists, low quality of services provided, limited speech therapy resources and monetary constraints were cited as obstacles to seeking speech therapy services despite some care centres offering subsidized or free services. The study concluded that addressing these therapy-related constraints is essential to providing effective care and delivery of speech and language therapy services to children with CP. The study recommended that the government should prioritize the recruitment of speech and language therapists in underserved areas, particularly in slum communities and collaborate with NGOs to conduct disability awareness campaigns in Kibra in order to combat stigmatization and ensure that community members are well-informed about the available services for children with CP. Article visualizations:
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".