The impact on life in people with dissociative seizures or drug-resistant epilepsy
Bibliographic record
Abstract
The aim of this study was to analyze and compare the impact on life in people with dissociative seizures (DS) and drug-resistant epilepsy (DRE). A qualitative approach was employed using the McGill Illness Narrative Interview, which was conducted and analyzed following thematic analysis principles. Ten women diagnosed with DS or DRE participated, all from underserved sectors in Argentina. Three major themes emerged from the interviews: (1) role of emotions (emotional experiences related to the disease, both preceding the seizure and as a consequence of them). Both groups reported unpleasant emotions as a consequence of seizures, such as fear, shame, and sadness. Emotional states, including stress and anxiety, were also described as seizure triggers in both conditions. (2) Impact on social interaction (the way in which the disease impacted on social relationships). Participants with DS experienced interpersonal conflicts, mistreatment, and disbelief more frequently than those with DRE, who reported a higher perception of overprotection and hesitancy to disclose their condition. Both groups acknowledged the importance of social support from family and friends. (3) Impact on daily life activities (the way in which people discontinued activities due to the disease or continued despite it). Seizures disrupted autonomy, work, and recreational activities, though some participants continued working despite limitations. These findings provide insight into the challenges of living with DS and DRE. A deeper understanding of these experiences can inform targeted interventions to improve the quality of life for these patient populations, particularly in resource-limited settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".