(004) “YOU NEVER KNOW WHAT IT LOOKS LIKE, UNTIL YOU LOOK AT IT”: PATIENT DEPICTIONS OF FEMALE GENITAL ANATOMY
Bibliographic record
Abstract
Abstract Introduction In recent years, there has been a growing interest in how patients employ health literacy, including basic anatomy knowledge, to navigate the health care system and communicate effectively with providers. Some empirical data suggests that female genital anatomy may often present a specific obstacle. While previous studies have called attention to this knowledge gap, there is little understanding of why this topic is particularly challenging for patients. Further, there has been no investigation of how patients visually understand and depict vulvar anatomy. Objective To inform future health literacy improvement efforts, we explored patient conceptualizations of the vulva through a drawing-based study. Methods Twenty obstetrics and gynecology patients at a tertiary care centre in Canada were asked to draw female external genital anatomy as part of a semi-structured interview. Participants ranged in age from 19 to 72 years old and had sought medical care for a variety of medical reasons, including pregnancy-related complications, childbirth, gynecologic cancer, and abnormal uterine bleeding. Each participant described their drawing to encourage collaborative meaning-making and give voice to what the drawing was intended to convey. The descriptions were audio-recorded and transcribed. Thematic analysis of both the descriptions and drawings was performed comparatively and iteratively, informed by principles of constructivist grounded theory and critical visual methodology. Results Participants were universally uncomfortable with the task of creating a drawing, regardless of age, health status, or previous employment. However, the investigation highlighted a relationship between previous exposure to images of female genital anatomy and ability to confidently capture vulvar landmarks through drawing. When uncertain, participants refused to abandon their task, and instead employed multiple artistic strategies in an attempt to produce a finished piece. Conclusions This study contributes to the body of evidence on genital self-image and the potential benefits of patient education to improve exposure to vulvar anatomy. However, we also provide new insight into how patients see female genital anatomy, with the opportunity to create specific educational interventions that address common stumbling blocks. Disclosure No.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".