The Use of Acceptance and Commitment Therapy in Oncology Settings: A Narrative Review
Bibliographic record
Abstract
Various psychotherapeutic approaches have been developed to address the psychosocial stressors and distress associated with cancer diagnosis and treatment. One such approach, Acceptance and Commitment Therapy (ACT), may be particularly well suited to people with cancer as it offers a model of healthy adaptation to difficult circumstances. This paper provides a description and theoretical rationale for using ACT in psychosocial oncology care that emphasizes emotional distress and cancer-related pain and provides a narrative review of the current state of evidence for this setting. Six studies met eligibility criteria for inclusion in the review. The research designs included one case study, three pre-post cohort studies, and two randomized controlled trials. Cancer diagnoses of patients included breast cancer, ovarian cancer, colorectal cancer, and mixed cancer populations at various stages of disease progression or recovery. ACT interventions demonstrated significant improvements in symptoms including quality of life and psychological flexibility as well as reductions in symptoms including distress, emotional disturbances, physical pain, and traumatic responses. Overall, although there is limited published research currently available, there is some evidence to support ACT as an effective psychotherapeutic approach for cancer patients. Further research is needed for different cancer populations across the illness trajectory. Barriers to implementation are discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".