A simulation-based intervention teaching seizure management to caregivers: A randomized controlled pilot study
Bibliographic record
Abstract
OBJECTIVES: To examine the effect of simulation-based seizure management teaching on improving caregiver competence and reported confidence with managing seizures. The authors hypothesized that simulation-based education would lead to a higher level of demonstrated competence and reported confidence in family members and caregivers. Simulation has not been previously studied in this context. METHODS: A two-group pre- and post-test experimental research design involving a total of 61 caregivers was used. The intervention was a simulation-based seizure curriculum delivered as a supplement to traditional seizure discharge teaching. Caregiver performance was analyzed using a seizure management checklist. Caregivers' perception of self-efficacy was captured using a self-efficacy questionnaire. RESULTS: Caregivers in the experimental group achieved significantly higher postintervention performance scores than caregivers in the control group in both premedication and postmedication seizure management (P<0.01). Additionally, they achieved significantly higher scores on the self-efficacy questionnaire including items reflecting confidence managing the seizure at home (P<0.05). CONCLUSION: Caregivers receiving the supplemental simulation-based curriculum achieved significantly higher levels of competence and reported confidence, supporting a positive relationship between simulation-based seizure discharge education, and caregiver competence and confidence in managing seizures. Simulation sessions provided insight into caregiver knowledge but, more importantly, insight into the caregiver's ability to apply knowledge under stressful conditions, allowing tailoring of curriculum to meet individual needs. These findings may have applications and relevance for management of other acute or chronic medical conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".