Systematic review of frequency of felt and enacted stigma in epilepsy and determining factors and attitudes toward persons living with epilepsy—Report from the International League Against Epilepsy Task Force on Stigma in Epilepsy
Bibliographic record
Abstract
OBJECTIVE: To review the evidence of felt and enacted stigma and attitudes toward persons living with epilepsy, and their determining factors. METHODS: Thirteen databases were searched (1985-2019). Abstracts were reviewed in duplicate and data were independently extracted using a standardized form. Studies were characterized using descriptive analysis by whether they addressed "felt" or "enacted" stigma and "attitudes" toward persons living with epilepsy. RESULTS: Of 4234 abstracts, 132 met eligibility criteria and addressed either felt or enacted stigma and 210 attitudes toward epilepsy. Stigma frequency ranged broadly between regions. Factors associated with enacted stigma included low level of knowledge about epilepsy, lower educational level, lower socioeconomic status, rural areas living, and religious grouping. Negative stereotypes were often internalized by persons with epilepsy, who saw themselves as having an "undesirable difference" and so anticipated being treated differently. Felt stigma was associated with increased risk of psychological difficulties and impaired quality of life. Felt stigma was linked to higher seizure frequency, recency of seizures, younger age at epilepsy onset or longer duration, lower educational level, poorer knowledge about epilepsy, and younger age. An important finding was the potential contribution of epilepsy terminology to the production of stigma. Negative attitudes toward those with epilepsy were described in 100% of included studies, and originated in any population group (students, teachers, healthcare professionals, general public, and those living with epilepsy). Better attitudes were generally noted in those of younger age or higher educational status. SIGNIFICANCE: Whatever the specific beliefs about epilepsy, implications for felt and enacted stigma show considerable commonality worldwide. Although some studies show improvement in attitudes toward those living with epilepsy over time, much work remains to be done to improve attitudes and understand the true occurrence of discrimination against persons with 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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".