Creating a virtual educational platform for families with epilepsy, based on their educational needs, beliefs, and attitudes towards epilepsy
Bibliographic record
Abstract
OBJECTIVE: Epilepsy affects 1-2 % of the global population, often beginning in childhood. To address the need for accessible education, we developed Knowledge2Empower, a virtual platform tailored to families managing pediatric epilepsy. METHODS: We conducted a survey of 92 families at the Alberta Children's Hospital Neuroscience Clinic to assess their educational preferences. Families with newer onset epilepsy prioritized basic epilepsy knowledge (p = 0.03), diagnostics (p = 0.005), and treatment options (p = 0.001), while those with longer-standing epilepsy were more interested in daily life with epilepsy. Based on these findings, we developed whiteboard-style educational videos, validated through feedback from healthcare providers, educators, and families. The videos were hosted on Thinkific.com, enabling personalized learning and progress tracking. RESULTS: The platform addresses the evolving needs of families at different stages of epilepsy management. Stakeholder validation confirmed its relevance and accessibility. CONCLUSION: Knowledge2Empower demonstrates the value of co-designing educational resources to improve understanding, reduce stigma, and support better epilepsy management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".