Assessing female genital satisfaction: validation of an Index of Female Genital Image in a French-Canadian sample of individuals with vulvas
Bibliographic record
Abstract
Recent research has focused on genital satisfaction, defined as one’s subjective experience of pleasure, comfort, and contentment regarding genital appearance and function. Genital satisfaction is associated with psychosexual well-being, body image, sexual satisfaction, and functioning. However, most measures of genital satisfaction focus on individuals with penises; measures available for individuals with vulvas present certain limitations or are only available in English. This study aimed to evaluate the psychometric properties of a short-scale measure of female genital satisfaction, inspired by the Index of Male Genital Image. A French version of Index of Female Genital Image (IFGI) was developed and validated within a sample of 652 French-speaking individuals with vulvas (Mage = 38.94, SD = 12.00), who completed an online survey on psychosexual well-being. Confirmatory factor and bivariate correlation analyses were performed to test the IFGI factorial structure and its relationship to psychosexual concepts. The IFGI presented satisfactory internal consistency and yielded two factors labelled External Appearance and Genital Function. Higher scores were associated with greater sexual satisfaction and functioning, and lower body shame (p < .010). Overall, the IFGI proves to be a reliable tool for measuring genital satisfaction in French-speaking individuals with vulvas, highlighting its importance for psychosexual well-being.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".