Psycho-oncology interventions from research to practice: the case of mindfulness-based interventions
Bibliographic record
Abstract
Abstract As a past recipient of the International Psycho-Oncology Society (IPOS) Bernard Fox Memorial Award, on the occasion of IPOS' 40th anniversary, Dr. Carlson reflects on the development, evaluation, and uptake of mindfulness-based interventions (MBIs) over the past 25 years as an example of a psychosocial oncology intervention that has moved from a complementary therapy generally outside of conventional medicine to a therapy endorsed in mainstream clinical practice guidelines. She summarizes the literature on MBIs for people with cancer and her team's contributions to the body of science now supporting the use of MBIs and reviews recent clinical practice guidelines from the American Society of Clinical Oncology and the National Comprehensive Cancer Network, which include recommendations for the use of MBIs for treating common symptoms in people with cancer including anxiety, depression, and fatigue as an example of a process that IPOS may support for other promising programs.
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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 | 0.102 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.054 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".