Supportive care for cancer-related symptoms in pediatric oncology: a qualitative study among healthcare providers
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to gain insight into the clinical experiences and perceptions that pediatric oncology experts, conventional healthcare providers, and complementary and alternative medicine (CAM) providers in Norway, Canada, Germany, the Netherlands, and the United States have with the use of supportive care, including CAM among children and adolescents with cancer. METHODS: A qualitative study was conducted using semi-structured in-depth interviews (n = 22) with healthcare providers with clinical experience working with CAM and/or other supportive care among children and adolescents with cancer from five different countries. Participants were recruited through professional associations and personal networks. Systematic content analysis was used to delineate the main themes. The analysis resulted in three themes and six subthemes. RESULTS: Most participants had over 10 years of professional practice. They mostly treated children and adolescents with leukemia who suffered from adverse effects of cancer treatment, such as nausea and poor appetite. Their priorities were to identify the parents' treatment goals and help the children with their daily complaints. Some modalities frequently used were acupuncture, massage, music, and play therapy. Parents received information about supplements and diets in line with their treatment philosophies. They received education from the providers to mitigate symptoms and improve the well-being of the child. CONCLUSIONS: Clinical experiences of pediatric oncology experts, conventional health care providers, and CAM providers give an understanding of how supportive care modalities, including CAM, are perceived in the field and how they can be implemented as adaptational tools to manage adverse effects and to improve the quality of life of children diagnosed with cancer and the families.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".