Designing a Measure of Body Image: Cognitive Interview Findings from an Adolescent and Young Adult Cancer Sample
Bibliographic record
Abstract
Purpose: A cancer diagnosis in adolescence and young adulthood significantly impacts a person’s quality of life, particularly concerning identity, self-esteem, and subsequently, body image. This study aims to develop a psychometrically-sound patient-reported outcome measure of body image for adolescent and young adult (AYA) oncology patients that was guided by the National Institutes of Health’s Patient-Reported Outcomes Measurement Information System® (PROMIS) Scientific Standards and our past concept elicitation interviews with AYAs. Methods: We conducted a multi-step approach involving item identification, refinement, generation; translatability and reading level review; and cognitive interviews. A purposive sample of 25 AYA patients participated, ensuring representation across educational levels, gender, treatment status, and cancer type. Results: Translatability and reading level reviews facilitated language adjustments. Cognitive interviews revealed that 76% of AYAs found the 50 candidate items assessing body image concerns to be easy to answer. AYAs reported that the body image items captured their lived experiences. Three items were excluded due to comprehension difficulties. Conclusion: This study addresses the critical gap in validated measures for assessing body image in AYA oncology patients. Interview findings provided evidence for the content validity and comprehensibility for 47 items assessing body image. The next steps involve large-scale psychometric testing to evaluate the reliability and validity of the body image items to form an item bank allowing the design of short forms or use of computerized-adaptive testing. Ultimately, this work lays the foundation for developing interventions to mitigate the impact of cancer on AYAs’ body image during diagnosis, treatment, and recovery.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".