Impact of Vulvovaginal Symptoms in Women Diagnosed with Cancer: A Psychometric Evaluation of the Day-to-Day Impact of Vaginal Aging Questionnaire
Bibliographic record
Abstract
Background: Vulvovaginal symptoms (VVS), such as dyspareunia, dryness, and itching, are common following cancer treatment and can negatively impact sexual well-being, daily activities, mood, self-concept, and overall quality of life. Information about the impact of VVS after cancer treatment is scarce, mainly because of a dearth of validated measures. This study aimed to validate the Day-to-Day Impact of Vaginal Aging (DIVA) questionnaire, which assesses impact of VVS on women's lives, in a sample of women with cancer. Methods: Women diagnosed with cancer ( n = 202) completed a questionnaire package, including the DIVA and measures of VVS, sexual function, and sexual distress. Data were collected as part of study evaluating an educational workshop. Confirmatory factor analysis (CFA) was used to examine whether the factor structure of the DIVA in this population reflected that of the original validation study. Results: CFA showed that the DIVA assesses VVS impact on four domains: activities of daily living, sexual functioning, emotional well-being, and self-concept and body image. All subscales showed excellent internal consistency reliability; however, item analyses indicated that items in the activities of daily living subscale showed very low means. Correlations with sexual function and distress provided evidence that the DIVA assesses impact of VVS. Conclusions: This is the first study aimed at validating the DIVA in women treated for cancer. Results provide evidence of the DIVA's utility in assessing the impact of VVS on four relevant domains. Although issues with certain scale items need to be resolved in future research, the DIVA provides opportunity to understand the impact of VVS after cancer treatment, to address unmet needs of 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".