Managing Cancer And Living Meaningfully (CALM): Phase 2 trial of a brief individual psychotherapy for patients with advanced cancer
Bibliographic record
Abstract
BACKGROUND: Advanced cancer brings substantial physical and psychosocial challenges that may contribute to emotional distress and diminish well-being. In this study, we present preliminary data concerning the effectiveness of a new brief individual psychotherapy, Managing Cancer And Living Meaningfully (CALM), designed to help individuals cope with this circumstance. AIM: To test the feasibility and preliminary effectiveness of CALM to reduce emotional distress and promote psychological well-being and growth. DESIGN: CALM is a brief, manualized, semi-structured individual psychotherapy for patients with advanced cancer. This study employed a phase 2 intervention-only design. The primary outcome was depressive symptoms and the secondary outcomes were death anxiety, attachment security, spiritual well-being and psychological growth. These were assessed at 3 months (t1) and 6 months (t2). Multilevel regression was used to model change over time. SETTING/PARTICIPANTS: A total of 50 patients with advanced or metastatic cancer were recruited from the Princess Margaret Cancer Centre, Toronto, Canada. RESULTS: A total of 39 patients (78%) were assessed at baseline, 24 (48%) at t1, and 16 (32%) at t2. Analyses revealed reductions over time in depressive symptoms: beta = -0.13, confidence interval (CI.95) = (-0.23, -0.022) and death anxiety: beta = -0.23, CI.95 (-0.40, -0.061); and an increase in spiritual well-being: beta = 0.14, CI.95 (0.026, 0.26). CONCLUSIONS: CALM may be a feasible intervention to benefit patients with advanced cancer. The results are encouraging, despite attrition and small effect sizes, and support further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".