Living Well With Uncertainty in Advanced, Metastatic or Incurable Cancers: A Pragmatic Feasibility Study of the Adapting to Life With Cancer Cognitive ExisteNtial Therapy (ACCENT)
Bibliographic record
Abstract
BACKGROUND: New treatments are contributing to individuals living longer with advanced, metastatic, or incurable (AMI) cancers. The impact of these treatments is unpredictable, resulting in considerable uncertainty for these patients. Currently, there are no interventions that effectively address uncertainty in AMI cancers. To fill this gap, we designed the Adapting to life with Cancer Cognitive ExisteNtial Therapy (ACCENT) intervention. AIM: To evaluate the feasibility, acceptability, and preliminary efficacy of ACCENT in AMI cancers. METHODS: ACCENT was delivered online for 6 consecutive weekly sessions of 1.5 hours to five groups of six to seven patients. Thirty-two patients were interested in participating, but 2 did not complete the assessments pre- or post-intervention. A pragmatic feasibility study was conducted using data collected in routine clinical practice. Participants completed the Intolerance of Uncertainty Scale-Short Form, the Generalized Anxiety Disorder scale, and the Impact of Events Scale before and after the intervention. Post-intervention, participants answered open-ended questions to assess acceptability, rated their perceptions of improvement and usefulness, and completed the Satisfaction with Therapy and Therapist Scale. RESULTS: ACCENT appears feasible with participants completing the intervention and all assessments between January 2022 and November 2023. It appears acceptable with an attrition rate of 12.5%, and a high degree of attendance and satisfaction. There was a non-significant decrease in intolerance of uncertainty, and a significant decrease in anxiety, and cancer-specific distress post-intervention. CONCLUSION: A randomized controlled pilot study is warranted to further evaluate ACCENT in patients with AMI cancers.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".