The clean vagina, the healthy vagina, and the dirty vagina: Exploring women’s portrayals of the vagina in relation to vaginal cleansing product use
Bibliographic record
Abstract
Vaginal cleansing products such as douches, sprays, wipes, powders, washes, and deodorants are part of a growing $2 billion industry in North America. Part of the appeal of these products is supposedly attaining vaginal cleanliness, which is marketed in association with product use. Although these products are promoted as healthy, medical research indicates potential health risks for some of these products (e.g. yeast infections, bacterial vaginosis, and disruption of the vaginal microbiota). Despite these risks, many women use these products. In this paper, we draw on interviews with women who use vaginal cleansing products to examine the ways in which particular portrayals of the vagina are connected with broader societal messages about female genitalia and with motivations to use vaginal cleansing practices. These portrayals include the healthy vagina, the clean vagina, and the dirty vagina. We show that although participants in our study valued both a clean vagina and a healthy vagina, when tension occurred between these two portrayals, participants prioritized vaginal cleanliness over vaginal health. We argue that this prioritization of the idealized clean vagina is connected to societal pressures of needing to attain unrealistic standards of vaginal cleanliness.
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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 | 0.004 | 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 | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".