Online interventions to address body image distress in cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Body image is a critical psychosocial issue for patients with cancer, because of the profound effects the disease and its treatment can have on appearance and bodily functioning. Adverse psychological effects of body image changes associated with cancer include debilitating levels of anxiety, social avoidance, depression, problems with intimacy and impaired sexuality, and feelings of shame/inadequacy. The construct of body image is increasingly recognized as complex and multifaceted from an embodied lens, creating more meaningful and efficacious interventions. Although there is some evidence now for in-person interventions, more research is needed in online and in-person interventions, particularly beyond what has been demonstrated in breast cancer. There is also need to address concerns around the practical and psychosocial barriers that can diminish access to, and participation in such individual or group interventions. Internet-based interventions offer opportunity for greater access to tailored psychosocial care. RECENT FINDINGS: An emerging conceptualization of body image for cancer patients is discussed. Internet-delivered interventions targeting body image are outlined; the majority are pilot trials and those developed for breast cancer patients. Challenges found in online interventions are also discussed. SUMMARY: Internet-delivered body image interventions would benefit from a broader conceptualization of body image, greater methodological rigor, and investigations focused on a broader range of cancer populations, beyond patients with breast cancer. Future research is needed to develop, test, and identify who can benefit from online interventions within cancer care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".