Health Care Providers’ Readiness to Adopt an Interactive 3D Web App in Consultations About Female Genital Mutilation/Cutting: Qualitative Evaluation of a Prototype
Bibliographic record
Abstract
BACKGROUND: Comprehensive and appropriate health care provision to women and girls with female genital mutilation or cutting (FGM/C) is lacking. Use of visuals in health care provider (HCP) consultations facilitates the communication of health information and its comprehension by patients. A web app featuring a 3D visualization of the genitourinary anatomy was developed to support HCPs in conferring clinical information about FGM/C to patients. OBJECTIVE: The aim of this study was to explore HCP perspectives on the use of visuals in discussion about FGM/C with their patients as well as to obtain their feedback on whether an interactive 3D web app showing the genitourinary anatomy would be helpful in patient consultations about FGM/C, identifying key features that are relevant to their clinical practice. METHODS: We evaluated the web app through a semistructured interview protocol with seven HCPs from various disciplines experienced in care for women and girls with FGM/C in migration-destination settings. Interviews were audio- and video-recorded for transcription, and were then analyzed thematically for contextualized data regarding HCPs' willingness to use a 3D web app visualizing anatomy in FGM/C consultations with patients. RESULTS: All but one of the seven participants expressed keen interest in using this web app and its 3D visuals of anatomy in FGM/C consultations with patients. Participants shared the common contexts for the use of visuals in health care for FGM/C and the concepts they are used to support, such as to help describe a patient's genitals after FGM/C and reinforce an understanding of clitoral anatomy, to illustrate the process of defibulation, or to explain the physiological effects of FGM/C. Participants also highlighted the benefit of using visuals that patients can relate to, expressing approval for the ability to customize the vulva by FGM/C subtype, skin tone, and complexity of the visual shown in the web app. Despite critiques that the visualization may serve to perpetuate idealistic standards for how a vulva should look, participants largely agreed on the web app's perceived usefulness to clinical practice and beyond. CONCLUSIONS: Evaluation of the web app developed in this study identified that digital tools with 3D models of the genitourinary anatomy that are accessible, informative, and customizable to any specific patient are likely to aid HCPs in communicating clinical information about FGM/C in consultations. Universal access to the web app may be particularly useful for HCPs with less experience in FGM/C. The app also prompts options for applications such as for personal use, in medical education, in patient medical records, or in legal settings. Further qualitative research with patients is required to confirm that adoption of the web app by HCPs in a consultation setting will indeed benefit patient care for women and girls with FGM/C.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".