A web-based resource for exercise training in children treated for brain tumours to improve cognitive sequelae: Development and usability
Bibliographic record
Abstract
Improving cognitive sequelae in children treated for brain tumours (CTBT) requires accessible interventions. While instructor-led exercise in a hospital setting is efficacious, it is not extended to communities. Objectives We aimed to (i) develop a website with educational resources/tools for community health and fitness professionals (HFP) to deliver exercise for CTBT in community settings to improve cognition and (ii) assess its usability by community HFP. It was hypothesized that the website would be learnable, clear, satisfactory and efficient to deliver exercise. Methods A scoping review determined the state of eHealth resources to support exercise for CTBT and identified knowledge and resource gaps. Three focus groups with HFP who served cancer survivors in hospital or community settings ( n = 13) identified user needs; content analysis identified themes. Gaps from the scoping review and themes from focus groups informed website content. A questionnaire assessed its usability by community HFP ( n = 4). Descriptive statistics inferred the website's learnability, clarity, satisfaction and efficiency. Open-ended responses identified issues. Results The scoping review revealed a lack of eHealth resources supporting exercise to improve cognition in CTBT and education for HFP to deliver exercise. Six themes were identified in the focus groups. HFP rated the website as sufficiently learnable, clear, satisfactory and efficient. Two minor issues were reported and addressed. Conclusion The website marks one of the first eHealth resources to increase accessibility of intervention to improve cognitive sequelae and ultimately quality of life in CTBT. HFP also gain access to education and tools to deliver exercise in community settings.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".