#ItsNotInYourHead: A Social Media Campaign to Disseminate Information on Provoked Vestibulodynia
Bibliographic record
Abstract
Provoked Vestibulodynia (PVD) is a type of localized vulvodynia (or pain in the vulva). The estimated prevalence of this condition is about 12% of the general population and approximately 20% of women under the age of 19. Many women who live with PVD suffer in silence for years before receiving a diagnosis. Whereas cognitive behavioral therapy (CBT) was already known to be effective for managing symptoms of PVD, there has recently been a published head-to-head comparison of CBT versus mindfulness-based therapy for the primary outcome of pain intensity with penetration. The trial revealed that both treatments were effective and led to statistically and clinically meaningful improvements in sexual function, quality of life, and reduced genital pain, with improvements retained at both 6- and 12-month follow-ups. We then undertook an end-of-grant knowledge translation (KT) campaign focused on the use of social media to disseminate an infographic video depicting the findings. Social media was strategically chosen as the primary mode of dissemination for the video as it has broad reach of audience, the public can access information on social media for free, and it presented an opportunity to provide social support to the population of women with PVD who are characterized as suffering in silence by starting a sensitive and empowering dialogue on a public platform. In this paper, we summarize the social media reach of our campaign, describe how and why we partnered with social media influencers, and share lessons learned that might steer future KT efforts in this field.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".