#PGADFacts: Results from a 12-month knowledge translation campaign on persistent genital arousal disorder/genito-pelvic dysesthesia (PGAD/GPD)
Bibliographic record
Abstract
Persistent genital arousal disorder/genito-pelvic dysesthesia (PGAD/GPD) is a distressing condition characterized by unwanted, persistent sensations of genital arousal that occur in the absence of corresponding subjective sexual arousal or desire. PGAD/GPD is associated with significant negative impacts on psychosocial well-being and daily functioning; however, PGAD/GPD remains largely unknown by both healthcare providers and the general public. This lack of awareness is a barrier to receiving healthcare and may lead to greater stigma associated with the condition. This project sought to develop and evaluate an empirically informed 12-month social media-based knowledge translation campaign on PGAD/GPD, titled #PGADFacts. One research-supported fact about PGAD/GPD was posted weekly to three social media platforms from November 2019 to December 2020. Social media analytics indicated that the campaign had significant reach (111,587 total views across platforms). An anonymous online feedback survey indicated that respondents who had seen the campaign reported greater knowledge about PGAD/GPD as compared to those who had not seen it. Responses also indicated high acceptance and appropriateness. Adoption rates (e.g., sharing information learned on or off social media), however, were low with negative emotions (e.g., embarrassment) being a common barrier. Results indicated that the #PGADFacts campaign was successful, however, additional strategies (e.g., paid ads, partnerships with influencers) may increase adoption and reach a broader audience. Greater awareness of PGAD/GPD is needed to increase recognition of the condition and access to care as well as to reduce associated stigma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".