Perceived stigma and school attendance among children and adolescents with epilepsy in South Western Uganda
Bibliographic record
Abstract
BACKGROUND: Epilepsy is a neurological disorder that has a high worldwide prevalence with eighty percent of the global burden being in low and middle-income countries. There is a high level of perceived stigma among children and adolescents with epilepsy, which has severe debilitating effects and affects school attendance. OBJECTIVE: To assess the effect of perceived stigma on school attendance patterns among children and adolescents with epilepsy. METHODS: We conducted a cross sectional study among 191 children and adolescents aged from 6-18 years with epilepsy at one large semi-urban hospital and a small rural health center in SouthWestern Uganda. Epilepsy-related perceived stigma was measured using the adapted Kilifi Stigma Scale of Epilepsy and school attendance patterns were assessed using a piloted investigator-designed questionnaire. RESULTS: Children with high-perceived stigma were more likely to have never attended school (13.8%) or started school late (average age 5.7 years) compared to those with low-perceived stigma (average age 4.9 years). Additionally, those with high epilepsy-related perceived stigma repeated classes 2.5 times more compared to those with low-perceived stigma. CONCLUSION: These preliminary findings suggest correlation between high-perceived stigma and disrupted school attendance patterns among children and adolescents with epilepsy, hence the need to address this social challenge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".