Confirmatory factor analysis of the Sexual Adjustment and Body Image Scale in women with gynecologic cancer
Bibliographic record
Abstract
BACKGROUND: There is evidence that treatment of gynecologic cancer (GC) negatively affects body image and sexuality. The Sexual Adjustment and Body Image Scale (SABIS-G) was developed to assess disturbances after diagnosis of GC. The objective of this study was to confirm the factor structure using a confirmatory factor analysis (CFA). METHODS: Women with a history of GC completed the SABIS-G, a 9-item self-report measure. Ninety randomly selected participants were used for the exploratory factor analysis (EFA). CFA was performed on the remaining participants (n = 204) to confirm the factor structure developed in the EFA against a one-factor model. Test-retest reliability between baseline and follow-up scores was assessed using the intraclass correlation coefficient. RESULTS: A total of 614 eligible patients were approached to participate: 398 (65%) consented to the study and 294 (74%) completed the SABIS-G. The median age was 53 years (range, 27-80 years) and the primary site of disease was: 120 (41%) uterine, 85 (29%) ovary, 82 (28%) cervix, and 7 (2%) other. A 2-factor structure was favored in the EFA, and the CFA fit indices indicated an excellent fit for the 2-factor measurement model (standardized root-mean-square residual = 0.05, non-normed fit index = 0.97, comparative fit index = 0.98). Internal consistency reliability was high for the Body Image (0.88) and Sexual Adjustment (0.91) subscales, as was test-retest reliability (0.89). CONCLUSIONS: These results confirm the 2-factor structure of the SABIS-G and provide evidence that this is a valid and reliable instrument to measure changes in body image and sexuality in women after a diagnosis of GC.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".