Navigating the Ghanaian health system: stories from families of children with intellectual and developmental disabilities
Bibliographic record
Abstract
This study explored the experiences of families in navigating the Ghanaian health system to address the general health needs of their children with intellectual and developmental disabilities (IDD). The sample involved 22 primary caregivers of children with IDD aged 3-18 years who participated in a semi-structured interview. The interviews were analyzed using the constant comparison analytical method. The findings highlighted key enablers and barriers related to three overarching themes: entry into the health system; consultation with health professionals; and service coordination. The findings showed that the families and their children gained entry into the health system in many health facilities. However, the families revealed that some facilities denied the children services, either because the children had difficulties following entry processing protocols or there were no health professionals willing to address the children's needs. Although health professionals perform their duties professionally during consultation and care administration in many cases, the families reported on some challenges. Service coordination was seamless in some facilities; however, the families reported on other facilities they accessed where service coordination was not seamless. The study findings illustrate that the experiences of families and their children with IDD in the Ghanaian health system may be mixed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".