Exploring the validity of the body image scale with survivors of breast cancer: A cognitive interview approach
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to explore the construct validity of the Body Image Scale for Cancer Questionnaire (BIS) using cognitive interviews. METHODS: Twelve breast cancer survivors participated in a cognitive interview while completing the BIS. Each participant was asked to think-out-loud while answering items, and an interviewer asked probing questions relating to the participants' comprehension, example retrieval, certainty of answer and other decision-making factors. Interviews were audio recorded and transcribed, and the data were analysed deductively and inductively. RESULTS: The participants' interpretations of the questions varied significantly. Several participants perceived the phrasing of some questions to be leading. The participants were able to provide examples of how their physical, physiological and body function affected their body image. The participants expressed positive attitudes towards, and gratitude for their body, which was not captured by the questionnaire. At times, the participants felt uncertain in how to respond appropriately to specific items, and the participants found some items challenging to answer. Finally, the BIS included sensitive questions that elicited emotional reactions and discomfort for some participants. CONCLUSION: The findings of this study provide insight into, and suggestions for potential questionnaire revisions that may enhance the validity and relevance of the BIS for use with breast cancer survivors.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".