Restoring body image after cancer (ReBIC): A group therapy intervention
Bibliographic record
Abstract
OBJECTIVES: Body image (BI) remains a significant survivorship challenge among breast cancer (BC) survivors. We describe an 8-week group intervention-restoring body image after cancer (ReBIC)-developed to target BI distress for BC survivors. METHODS: The intervention was informed by interviews with BC survivors and by a descriptive, exploratory approach which adapted guided imagery exercises to address BI. Educational material was selected to address sociocultural factors that may contribute to BI distress and affect adjustment. Videotape reviews and content analyses further refined the intervention. RESULTS: The intervention incorporates three active components: psychotherapeutic group principles; guided imagery exercises to address BI; and psychoeducation on relevant socialization factors and gender-based messages internalized by women in Western society. The therapeutic group was a supportive and effective way to assist BC survivors to gain insight on BI impacts, their histories, and relevant sociocultural factors contributing to BI distress. The group also facilitated the working through of grief over multiple losses. Guided imagery was well-received, and appeared to help survivors identify negative and emerging self-schema, as well as facilitate new self-views. Specific themes included negative emotions associated with an altered body and self, grief and loss, isolation, difficulties with sexual intimacy, relationship challenges, and uncertainty around sense of self and future. CONCLUSION: An empirically tested group therapy intervention is described and has implications for survivorship programs to help address BI-related challenges. Future work could consider testing a similar approach tailored for other cancer populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".