Patients diagnosed with non-epileptic seizures: their perspective and experience
Bibliographic record
Abstract
Patients that suffer from psychogenic non-epileptic seizures are confronted with many obstacles in seeking effective treatment for their illness. Underlying many of these obstacles is the divergence between the medical model and the patient's perception of their illness. The objective of this qualitative study is to elucidate, through semi-structured interviews, the subjective illness and treatment experience of these patients, in order to answer the research question: How do non-epileptic seizure patients make sense of their illness experience? This may allow a better understanding of the impediments to proper care that the patients encounter. The results showed that the participants that implicitly incorporated epilepsy as an illness prototype demonstrated less effective treatment expectations and imposed greater life constraints on themselves, than the participant that utilized anxiety attacks as an illness prototype. The participants that defined an explanatory model with a psychosocial basis for illness onset were receptive and demanding of psychotherapeutic intervention. The importance of early diagnosis and improved diagnostic strategies is emphasized. Two overarching interconnected themes that emerged, loss of control and an inability to communicate appeared to characterize the underlying internal struggle that permeated the illness and treatment experience of the study participants.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".