13 / IS EMOTION REGULATION ASSOCIATED WITH CANCER-RELATED PSYCHOLOGICAL SYMPTOMS?
Bibliographic record
Abstract
Background: Breast cancer patients frequently report a combination of psychological symptoms including anxiety, depression, fear of cancer recurrence (FCR), insomnia, fatigue, pain, and cognitive impairments. In the general population, emotion regulation (ER) is considered a central mechanism underlying the development of psychological disorders. However, the relationships between ER and cancer-related psychological symptoms have received little attention. Objectives: To examine the cross-sectional and prospective relationships between subjective (cognitive reappraisal, expressive suppression and experiential avoidance) and objective measures (high frequency heart rate variability [HF-HRV]) of ER and a set of psychological symptoms (anxiety, depression, FCR, insomnia, fatigue, pain, and cognitive impairments) among women receiving radiation therapy for breast cancer. Method: 81 participants completed a battery of self-report scales before (T1) and after (T2) radiotherapy. HF-HRV at rest was measured at T1. Results: Canonical correlation analyses revealed that higher levels of experiential avoidance and expressive suppression were cross-sectionally associated with higher levels of all symptoms at T1 (R = .72, p < .0001) and at T2 (except pain; R = .75, p < .0001). Higher levels of suppression and reappraisal measured at T1 were marginally associated with reduced FCR and with increased depression and fatigue between T1 and T2 (R = .56, p = .07). Conclusions. These results suggest that maladaptive ER strategies, assessed subjectively, may act as a transdiagnostic mechanism underlying several psychological cancer-related symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".