Feasibility and acceptability of i‐Restoring Body Image after Cancer (i‐ReBIC): A pilot trial for female cancer survivors
Bibliographic record
Abstract
OBJECTIVE: This pilot study aimed to evaluate the feasibility, acceptability, and psychosocial outcomes of a text-based online group therapy intervention, i-Restoring Body Image after Cancer (i-ReBIC). i-ReBIC was developed to reduce body image distress and psychosexual dysfunction among women diagnosed and treated for breast or gynecological cancer. METHODS: i-ReBIC was adapted from an empirically tested face-to-face group therapy intervention, ReBIC. Over the 8-week intervention, participants engaged in 90-minute weekly text-based online discussions. Each week, a new topic associated with reconnecting to the body, adjusting to a postcancer identity, and improving psychosexual functioning was addressed. Homework assignments included readings, guided imagery exercises, and journaling. RESULTS: Sixty women with cancer enrolled in the pilot study. Among them, 47 completed the intervention, and 44 filled out all prestudy and poststudy questionnaires. Ninety-three percent of participants (n = 41) were satisfied and reported that it met their expectations. Eighty percent of participants (n = 35) reported no technical difficulties during the intervention. Preoutcome and postoutcome measures on body image distress and experience of embodiment showed statistically significant improvements. Psychosexual distress and quality of life also showed improvements but were not statistically significant. CONCLUSIONS: This study suggests that i-ReBIC is feasible, well accepted, and effective in addressing persistent body image concerns experienced by women treated for breast or gynecological cancer. As an online group therapy, i-ReBIC can expand the reach of its original face-to-face intervention by mitigating barriers and improving access to care in a cost-effective manner.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".