Prioritizing Solutions and Improving Resources among Young Pediatric Brain Tumor Survivors: Results of an Online Survey
Bibliographic record
Abstract
Pediatric Brain Tumor Survivors (PBTS) often experience social, academic and employment difficulties during aftercare. Despite their needs, they often do not use the services available to them. Following a previous qualitative study, we formulated solutions to help support PBTS return to daily activities after treatment completion. The present study aims to confirm and prioritize these solutions with a larger sample. We used a mixed-methods survey with 68 participants (43 survivors, 25 parents, PBTS' age: 15-39 years). Firstly, we collected information about health condition, and school/work experience in aftercare. Then, we asked participants to prioritize the previously identified solutions using Likert scales and open-ended questions. We used descriptive and inferential statistics to analyze data, and qualitative information to support participants' responses. Participants prioritized the need for evaluation, counseling, and follow-up by health professionals to better understand their post-treatment needs, obtain help to access adapted services, and receive information about resources at school/work. Responses to open-ended questions highlighted major challenges regarding the implementation of professionals' recommendations at school/work and the need for timely interventions. These results will help refine solutions for PBTS and provide key elements for future implementation. Translating these priorities into action will need further work involving professionals and decision makers.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".