Psychological treatment outcomes for cancer patients: what do meta‐analyses tell us about distress reduction?
Bibliographic record
Abstract
OBJECTIVE: The effectiveness of psychological treatment for distress reduction in cancer patients has been frequently studied and reviewed in systematic reviews but reviewer conclusions vary considerably. Clear and consistent evidence is needed to assist clinicians and administrators with their decision-making. We hypothesized that uneven handling of confounding methodological features are at least partly the reason for disagreements and reviewed the literature in this light. METHOD: A systematic review of 14 published meta-analyses was conducted to determine whether due consideration of moderating variables in psycho-oncological treatments permits clearer recommendations. Quality of the reviews, treatment type, dosage, therapist qualities, outcomes at follow-up, and screening versus not screening for elevated distress were examined as moderator variables. RESULTS: Treatment effects are consistently positive but also vary greatly in magnitude. There is lacking evidence for many important questions, in particular, differential treatment effects for different cancer types and stages. Regarding moderators of outcome, quality of review had no impact on results for depression but including lower quality reviews actually lead to underestimation of treatment effects for anxiety. The most potent negative moderator variable, however, is a floor effect that arises when patients are recruited for treatment studies without being selected for high levels of distress. Such indiscriminate recruitment is very frequent in psycho-oncology and leads to small reported treatment effects; when, however, patients are first screened for elevated distress, the ratio of observed treatment effects sizes is roughly three times greater. CONCLUSION: Sweeping judgments about the effectiveness of psycho-oncological treatments for distress reduction are somewhat misleading and counter-productive. Among moderator variables, floor effects are particularly pervasive and have a large suppressor effect on observed outcomes.
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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 | 0.069 | 0.222 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.026 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".