Bibliographic record
Abstract
Epilepsy directly affects 50 million people worldwide. Most can achieve excellent seizure control; however, people living with epilepsy continue to suffer from enacted or perceived stigma that is based on myths, misconceptions and misunderstandings that have persisted for thousands of years. This paper reviews the frequency and nature of stigma toward epilepsy. Significant negative attitudes prevail in the adolescent and adult public worldwide leading to loneliness and social avoidance both in school and in the workplace. People with epilepsy are often wrongly viewed as having mental health and antisocial issues and as being potentially violent toward others. Twenty-five percent of adults having epilepsy describe social stigma as a result of their epilepsy. They fear rejection and often feel shame or loneliness from this diagnosis. The psychosocial and social impact of epilepsy is significant. Yet few specific interventions have been demonstrated to alter this perception. The effect on public education is primarily short-term, while change over the long-term in attitudes and inaccurate beliefs have not presently been proven effective. School education programming demonstrates improved knowledge and attitude a month after a classroom intervention, but persisting change over a longer period of time has not been evaluated. In-depth adult psycho-educational programs for adults with epilepsy improves knowledge, coping skills and level of felt stigma. However these gains have not demonstrated persistence over time. Myths, misconceptions and misunderstandings about epilepsy continue and programs aimed at increasing knowledge and reducing negative public attitudes should be enhanced.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".