Exploring Physical Self-Perceptions Among Survivors of Adolescent and Young Adult Cancer
Bibliographic record
Abstract
This study explored the factor structure of four subscales from the Physical Self-Description Questionnaire-Short Form (PSDQ-S). Associations between subscales and personal and medical factors were also examined. The analytic sample consisted of 89 survivors of adolescent and young adult cancer (Mage at time of study = 32.96 ± 4.37 years; Mage at diagnosis = 31.16 ± 4.84 years; 75.3% female). Confirmatory factor analysis suggested a reasonable fit to the data, indicating that the PSDQ-S subscales examined could be used in future investigations with this population [χ2(38) = 46.9, p = 0.15; Root Mean Square Error of Approximation = 0.05, 90% confidence interval = 0–0.10; Comparative Fit Index = 0.97; Standardized Root Mean Square of the Residuals = 0.07]. Multiple linear regressions showed that personal and medical factors accounted for a significant amount of variance in the body fat subscale, with female sex and higher body mass index being significantly associated with lower positive perceptions about the amount of one's body fat. More research examining the factor structure of the PSDQ-S subscales is warranted, and future investigations exploring personal, medical, and modifiable factors associated with physical self-perceptions should be conducted.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".