Designing a resilience-based intervention program for children with cancer and their families: a study protocol
Bibliographic record
Abstract
Background: Advances in pediatric oncology have significantly increased survival rates, yet have introduced challenges in managing long-term treatment side effects. This study process introduces an interdisciplinary clinical intervention program rooted in the family resilience framework, aimed at improving well-being across the cancer trajectory for children and their families, especially those in Canadian communities far from specialized oncology centers with limited access to resources. Methods: Employing an intervention mapping approach, this program collaboratively involves patients, families, professionals, and researchers. It aims to identify vulnerability factors, establish a logic model of change, and devise comprehensive strategies that include professional interventions alongside self-management tools. These strategies, tailored to address biopsychosocial and spiritual challenges, are adapted to the unique contexts of communities distant from specialized cancer treatment centers. A mixed-methods approach will evaluate program effectiveness. Expected results: Anticipated outcomes include the empowerment of families with self-management tools and professional support, designed to mitigate biopsychosocial and spiritual complications. By addressing the specific needs and limitations of these communities, the program strives to improve the overall health and well-being of both undergoing treatment and survivorship phases. Discussion: By focusing on comprehensive care that includes both professional interventions and self-management, this initiative marks a significant shift toward a holistic, family-centered approach in pediatric oncology care for remote communities. It underlines the necessity of accessible interventions that confront immediate and long-term challenges, aiming to elevate the standard of care by emphasizing resilience, professional support, and family empowerment in underserved areas.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".