Perceived and self-stigma in people with epilepsy in East Africa: Systematic review and meta-analysis
Bibliographic record
Abstract
Background People with stigmatizing conditions associated with epilepsy encounter many difficulties in their daily lives and are more likely to have low self-esteem, low levels of hope, internalize negative attitudes, decrease adherence to treatment, and experience unemployment. The purpose of this study was to quantify the extent of perceived stigma and self-stigma among people with epilepsy. Methodology This systematic review and meta-analysis followed the Preferred Reporting Item Review and Meta-analysis (PRISMA) guideline. PubMed, PsycINFO, Web of Science, Cochrane Library, Google Scholar, and HINARI were major search databases. The included literature reports the prevalence of perceived stigma and self-stigma among people with epilepsy in East Africa. The quality of each study was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS). Data were extracted using a Microsoft Excel spreadsheet, and data analysis was performed using STATA version 11. The pooled prevalence of perceived stigma and self-stigma was determined using a random effect model. Heterogeneity between studies was checked using the I 2 statistical test. Publication bias was checked using Egger's statistical test and funnel plot. Results The pooled prevalence of perceived stigma and self-stigma in people with epilepsy was 43.9% with a 95% CI (29.2, 58.7) and 41.2% with a 95% CI (12.1, 70.3), respectively. Based on the country, sub-group analysis revealed that the prevalence of perceived stigma among people with epilepsy shows a notable difference between the countries. In Ethiopia, the prevalence was 51.8% with a 95% CI of 29.8 to 73.8; in Uganda, 39.4% with a 95% CI of 27.1 to 51.3; in Tanzania, 27.4% with a 95% CI of 27.9 to 36.9; and in Kenya, 33.2% with a 95% CI of 28.2 to 38.2. Conclusion Roughly 30% of people with epilepsy experience self-stigma, while approximately 44% of people with epilepsy experience perceived stigma. As a result, the relevant authorities ought to focus on reducing the prevalence of stigma among people who have epilepsy.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".