A scoping review of measures used to assess body image in women with breast cancer
Bibliographic record
Abstract
OBJECTIVE: The emergence of body image studies in the oncology setting has led to the use of numerous measures to assess different dimensions of body image. The present study is a scoping review of the literature on body image in women with breast cancer to describe: measures used to assess body image in women with breast cancer, dimensions the measures used tap into, and gaps and issues needing attention going forward. METHODS: Three databases were searched for peer-reviewed original studies that had: (1) full-texts available in English; (2) focused on women with breast cancer; and (3) assessed body image. RESULTS: The search yielded 3,729 peer-reviewed articles; after screening, 562 articles met inclusion criteria. Of the 88 measures used, 28 were used in more than two studies and analyzed herein. The European Organization for Research and Treatment of Cancer Breast Cancer-Specific Quality of Life Questionnaire constituted the most frequently used measure. Most measures used focused on the affective dimension of body image (n = 24/28, 85.7%), followed by the cognitive (n = 20/28, 71.4%), behavioral (n = 13/28, 46.4%), and perceptual dimensions (n = 13/28, 46.4%). CONCLUSIONS: This review provides a current summary of measures used to assess body image in women with breast cancer. Although some further development and refinement of body image measures could benefit the field, depending on the questions researchers or clinicians seek to answer, there are many available for use. Future research should use these measures to assess the effectiveness of interventions aimed at improving body image in women with breast cancer across the lifespan.
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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 | 0.026 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".