What Do Patients Want? A Needs Assessment of Vulvodynia Patients Attending a Vulvar Diseases Clinic
Bibliographic record
Abstract
INTRODUCTION: Vulvodynia is a chronic pain disorder that negatively impacts the quality of life of affected women. AIM: The goal of this study was to identify unmet needs among localized provoked vulvodynia patients. METHODS: A qualitative needs assessment was performed in a subspecialized vulvar clinic in a single academic institution in Canada. Semistructured interviews were conducted, recorded, and analyzed using the constant comparative method of grounded theory to identify common themes. MAIN OUTCOME MEASURES: Interviews were conducted until theme saturation was achieved. RESULTS: A diverse sample of 8 patients completed all components of the study. The most prominent unmet needs raised by patients in their interviews were categorized into 3 main themes: (1) challenges related to obtaining a diagnosis of vulvodynia and finding practitioners who are knowledgeable about vulvodynia; (2) challenges related to the current impact of the disease physically, emotionally, and in social relationships with patients' intimate partners; and (3) barriers to adherence with recommended therapy. Solutions recommended by patients include better education of physicians regarding vulvodynia and the development of multidisciplinary programs that provide access to physiotherapy, sex therapy, mindfulness and psychology services on-site, information classes for new patients, and the creation of peer support networks for patients and their partners. CONCLUSION: A patient-focused needs assessment suggests optimal vulvodynia care requires better education of physicians and a multimodal approach to therapy, ideally with multiple services offered in 1 location.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".