Physical activity in young people with epilepsy: development of an informational software application
Bibliographic record
Abstract
BACKGROUND: Research suggests that physical activity (PA) has potential benefits for young people with epilepsy (YPE); however, further studies are needed to explore how healthcare providers can promote effective interventions in collaboration with YPE and their families. OBJECTIVE: This knowledge translation project aimed to understand healthcare providers' perspectives on PA discussions and design a tool to support PA engagement among YPE. METHODS: Surveys of specialists, nurses, nurse practitioners, and trainees in pediatric neurology and epilepsy programs in the USA, Canada, Israel and Turkey were conducted to assess current PA discussions in clinical care. Inductive content analysis of responses was guided by eight themes from our previous research. Findings from the surveys and prior focus groups with YPE and their parents informed the development of a web app. RESULTS: Among 73 respondents, healthcare providers emphasized the need for an accessible, easy-to-use online tool that YPE can understand and take home to review. They identified 23 activities YPE discussed during clinical visits. Content analysis of survey results yielded a Krippendorff's Alpha of 0.846 (95% CI: 0.717-0.967) and informed the app content. Many providers reported that time constraints and limited resources hinder PA discussions in clinical settings. CONCLUSION: Healthcare providers suggest that an accessible, user-friendly, cost-free tool may help address PA concerns and promote active lifestyles for YPE and their families. This project describes the early stages of using a translational research model to bridge evidence and practice, turning research findings into actionable interventions for patients and families.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".