Effect of designing a nursing educational protocol on frequency of epileptic attacks
Bibliographic record
Abstract
Background and objective: Educating the epileptic patient represents a critical part of quality care and is considered a therapeutic outcome for those patients. This study aimed to determine epileptic patients' knowledge regarding the disease and frequency of seizure attacks prior to the implementation of the educational protocol and to evaluate the effect of an educational protocol on epileptic patient's knowledge and frequency of seizure attacks.Methods: Design: A quasi-experimental (pre-posttest) design was submit to meet the aim of the study. Patients: Sixty convenient male and female adult seizure patients were their age ranged between 18-64 years. Setting: This study was carried out in Neurological department and neurology out-patient clinic at Minia University Hospital. Tools of data collection: Four tools were utilized; Pre/posttest questionnaire sheet, Perceived Stress Scale, Epworth Sleepiness Scale, and patient teaching booklet.Results: About 86.7% of the study sample has an unsatisfactory level of knowledge about seizures and 13.3% have satisfactory knowledge about seizures before the implementation of the protocol. While in posttest II it was noticed that 90.0% have satisfactory knowledge about seizures. There was a high positive significant correlation between perceived stress scale and frequency of attacks among the study subjects.Conclusions: An improvement in seizure patients’ knowledge after the implementation of the educational protocol. An improvement in patient’s knowledge positively reflected on minimization and control frequency of seizure attacks. Recommendation: An educational and training protocol should be planned in a continuous manner and offered on regular basis to seizure patients.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".