Being physically active with epilepsy: Insights from young people and their parents
Bibliographic record
Abstract
INTRODUCTION: Researchers have called for innovative tailored interventions to address specific challenges to physical activity (PA) engagement for young people with epilepsy (YPE). Working with YPE and their parents, this study aimed to identify barriers and facilitators to adoption and maintenance of PA among YPE prior to and during the COVID-19 pandemic. METHODS: Ten YPE (all female) and their 13 caregivers, and five additional caregivers to males (N = 18; 72% mothers), completed virtual focus group sessions prior to and during the COVID-19 pandemic. Trained Child Life specialists asked questions about barriers and facilitators of PA engagement experienced by YWE, which included a specific focus on the impact of epilepsy. RESULTS: Thematic analysis of the data identified both epilepsy-specific and generic themes that impact PA participation among YPE. These included: (i) epilepsy experience/impact and accommodation; (ii) safety precautions; (iii) concern about seizures; (iv) social connections and acceptance; (v) parent and family support; (vi) intrapersonal self-regulation and motivation; (vii) health benefits; and (viii) key factors in common with all youth. CONCLUSION: This study provides valuable insight into diverse social-ecological health factors that impact PA participation among YPE from two key stakeholder perspectives (YPE and their caregivers). By understanding these lived experiences, providers can better tailor individual support for YPE and their families to foster and maintain a healthy active lifestyle.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".