Feasibility and acceptability of study methods and psychosocial interventions for body image targeting women diagnosed with breast cancer: a protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Improving body image may help to enhance the quality of life of women diagnosed with breast cancer. Although evidence suggests psychosocial interventions can effectively improve body image in this population, no review to date has assessed their feasibility or acceptability. This manuscript reports the protocol for a review summarising current evidence for the feasibility and acceptability of psychosocial interventions for body image targeting women diagnosed with breast cancer and the study methods used to evaluate the interventions in question to provide recommendations to optimise the success and sustainability of psychosocial interventions for body image and future studies. Results will also help to identify gaps in the existing evidence to provide direction for future research. METHODS AND ANALYSIS: We searched the following databases for articles published in the English language from January 2000 to June 2021 using a systematic search strategy: MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, PsychINFO and EMBASE. This search will be supplemented with a manual search of reference lists from relevant systematic reviews and included articles. Eligible studies will include peer-reviewed publications reporting on feasibility and acceptability in the evaluation of psychosocial interventions for body image targeting women diagnosed with breast cancer. All study designs are eligible, although articles are required to have reported on an intervention evaluation. Two reviewers will independently carry out study selection, extraction of quantitative and qualitative data and quality assessment. Data will be summarised in a narrative review and thematic analysis. ETHICS AND DISSEMINATION: No ethical approval is required because this is a protocol for a systematic review. On completion, results will be submitted for publication in a peer-reviewed scientific journal and for presentation at a relevant conference. TRIAL REGISTRATION: This protocol has been registered in the Prospective Register of Systematic Reviews international registry (ID: CRD42021269062, 11 September 2021).
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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 | 0.225 | 0.223 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.019 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.055 | 0.015 |
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".