Assessing the impact of multidrug-resistant tuberculosis in children: an exploratory qualitative study
Bibliographic record
Abstract
BACKGROUND: While the prevalence of multidrug-resistant (MDR) tuberculosis (TB) is high among children in the Western Cape of South Africa, the psychosocial implications of treatment for children with MDR-TB remain poorly understood. We sought to explore how MDR-TB and its treatment impact children on an individual, familial, and social level. METHODS: Semi-structured interviews were conducted with 20 children and caregivers purposively sampled from a prospective clinical cohort of children. The sample was stratified by age at the start of treatment (children >10 years, and 5-10 years). Caregiver proxy interviews were conducted with younger children, supplemented with child interviews; older children were interviewed directly, supplemented with caregiver proxy interviews. Data were analysed using grounded theory. RESULTS: Findings revealed pill volume and adverse effects produced significant physical, psychological and academic disturbances in children. Adverse effects related to the medication were important obstacles to treatment adherence. While there appear to be no long-lasting effects in younger children, a few older children showed evidence of persisting internalised stigma. Caregivers suffered important treatment-related financial and psychological costs. Community support, notably through the continued involvement of children in strong social networks, promoted resilience among children and their families. CONCLUSIONS: We found that the current treatment regimen for childhood MDR-TB has significant psychological, academic, and financial impacts on children and their families. There is a need for psychosocial support of children and caregivers to mitigate the negative effects of community stigma, and to manage the stressors associated with chronic illness.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".