Unfavorable public attitude toward people with epilepsy in Ethiopia: A systematic review and meta‐analysis study
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis aims to show the pooled prevalence of unfavorable public attitude toward people with epilepsy (UPATPWE) as well as the effect estimates of associated factors in Ethiopia. METHODS: Between December 1 and 31, 2022, we searched for the English version of published research reports on public attitude toward epilepsy in Ethiopia in PubMed/Medline, Science Direct, Cochrane Library, Google Scholar, and PsycINFO. The research reports' quality was assessed using the Newcastle-Ottawa Scale. We extracted the relevant information from the searched papers in a Microsoft Excel format and imported it to STATA version 15.0, for analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reports guideline was used. A random-effects meta-analysis model was used to estimate the Der Simonian and Laird's pooled prevalence of unfavorable public attitude and its associated factors. RESULTS: Nine out of the accessed 104 research papers meeting the pre-specified criteria were included in this study. The overall pooled prevalence of UPATPWE in Ethiopia is 52.06 (95% CI: 37.54, 66.59), resulting in excommunication, physical punishments, and assaults against people with epilepsy as well as frequent lack of diagnosis and proper treatment. The pooled effect estimates for witnessing a seizure episode were done and it was (AOR = 2.70 [95% CI: 1.13, 6.46]). SIGNIFICANCE: As interventions and new strategies to change attitudes and facilitate a supportive, positive, and socially inclusive environment for PWE may root in education and scientific research outputs, our result hopefully evokes the policy makers' attention for building a well-designed and comprehensive health education and campaign strategy.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".