Mindfulness Practices for Children and Adolescents Receiving Cancer Therapies
Bibliographic record
Abstract
Background: Mindfulness is our innate capacity to pay full, conscious, and compassionate attention to something in the moment. It is also a skill that can be strengthened by mental practice. More recently, mindfulness-based interventions (MBIs) are identified within clinical practice guidelines as an intervention in the treatment of certain symptoms for children with cancer. However, there is little guidance available on the practice of using MBIs in the pediatric oncology population. The aim of this paper is to provide an overview of mindfulness, highlights symptoms where mindfulness practices may be of benefit, identifies trauma-sensitive considerations, and provides examples of MBIs that may be considered in the context of pediatric oncology. Methods: Collaboration of expert opinion, which included The Mindfulness Project Team, has enabled this collective informative paper. Results: Mindfulness has been recommended to help with the symptom of fatigue in children with cancer. Emotional symptoms such as anxiety, sadness, and anger may also benefit from the use of MBIs. Ideal MBIs for this population may include mindful movement, mindfulness of the senses, mindfulness of breath, mindfulness of emotions, and the body scan. These approaches can easily be adapted according to the age of the child. Many approaches have been administered with minimal training, with very few requiring a facilitator. However, hospitals have started to incorporate mindfulness experts within their care provision. Conclusion: Future research should continue to investigate the use of MBI programs for children with 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".