At a loss for words: A qualitative exploration of female genital knowledge among obstetrics and gynecology patients
Bibliographic record
Abstract
Patient health literacy, including basic anatomy knowledge, leads to improved communication and better health outcomes. Limited empirical data suggests that external genital anatomy may represent a particular knowledge gap. To inform future health literacy improvement efforts, we explored patient perspectives about how gynecologic anatomical literacy is generated and applied. Twenty semi-structured interviews with obstetrics and gynecology patients at a tertiary care centre were conducted to explore their knowledge of female genital anatomy and the origins of that knowledge. Thematic analysis was performed comparatively and iteratively, informed by principles of constructivist grounded theory. Participants highlighted an overwhelming lack of health education and high levels of internalized shame, leaving them ill-equipped to engage in conversations about their genitalia with healthcare providers. To combat this, participants attempted to construct knowledge for themselves; however, many grappled to identify reliable sources of information and felt uncertainty when communicating about their bodies. These findings contribute to an ongoing conversation about how an avoidance of naming may perpetuate the passivity and embarrassment that women experience regarding their reproductive health. Healthcare providers are well-situated to improve patient self-perception by using purposeful language and working to address both patient knowledge and activation.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".