Effect of teaching strategies on adherence to antiepileptic drugs and recurrence of seizures among epileptic patients
Bibliographic record
Abstract
Background and objective: Adherence to medications is the backbone to effectiveness of a treatment. Adherence to antiepileptic drugs (AEDs) is essential to prevent the risk of seizures recurrence. The aims were to study the effect of teaching strategies on adherence to antiepileptic drugs, recurrence of seizures, and identify factors affecting the adherence level among epileptic patients.Methods: Research design: Quasi-experimental design. Setting: Neurology Department at Neurological and Psychiatric Assiut University Hospital. Sample: A purposive sample of sixty male and female adult patients diagnosed with epilepsy. Tools: Tool I-Patient assessment sheet. Tool II-Morisky Medication Adherence Scale. Tool III-Liverpool Seizure Severity Scale.Results: There was a statistically significant difference between pre and post applying of teaching strategies as regard drug adherence and recurrence of seizures among epileptic patients (p < .001). Also, forgetfulness, side effects of medications, and absence of family or friend were the main factors of non- adherence to AEDs.Conclusions and recommendations: Teaching strategies had statistical significant effects on adherence to antiepileptic drugs and on reducing recurrence of seizures among epileptic patients. Simple educational pamphlet for epileptic patients and their family members to improve adherence to AEDs should be available in Neurology Department and Outpatient Neurology Clinics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".