Understanding Effective Delivery of Patient and Family Education in Pediatric Oncology: A Systematic Review From the Children’s Oncology Group
Bibliographic record
Abstract
A diagnosis of childhood cancer is a life-changing event for the entire family. Parents must not only deal with the cancer diagnosis but also acquire new knowledge and skills to care safely for their child at home. Best practices for delivery of patient/family education after a new diagnosis of childhood cancer are currently unknown. The purpose of this systematic review was to evaluate the existing body of evidence to determine the current state of knowledge regarding the delivery of education to newly diagnosed pediatric oncology patients and families. Eighty-three articles regarding educational methods, content, influencing factors, and interventions for newly diagnosed pediatric patients with cancer or other chronic illnesses were systematically identified, summarized, and appraised according to the Grading of Recommendations Assessment, Development, and Evaluation criteria. Based on the evidence, 10 recommendations for practice were identified. These recommendations address delivery methods, content, influencing factors, and educational interventions for parents and siblings. Transferring these recommendations into practice may enhance the quality of education delivered by health care providers and received by patients and families following a new diagnosis of childhood cancer.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".