#DebunkingDesire: Sexual Science, Social Media, and Strategy in the Pursuit of Knowledge Dissemination
Bibliographic record
Abstract
Approximately 1 in 3 women experience low sexual desire. Despite this being a common concern, many women never seek professional help for their difficulties and will instead turn to online resources for information. We sought to address this need for digitally-accessible, evidence-based information on low sexual desire by creating a social media Knowledge Translation (KT) campaign called #DebunkingDesire. Our team led a 10 month social media campaign where our primary outcomes for the campaign were impressions, reach, and engagement. We generated over 300,000 social media impressions; appeared on 11 different podcasts that were listened to/downloaded 154,700 times; hosted and participated in eight online events; and attracted website users from 110 different countries. Over the course of the campaign we compiled lessons learned on what worked for disseminating our key messages and the importance of creating community for this population. These findings point to the utility of using social media as part of KT campaigns in sexual health, and to the importance of collaborating with patient partners and considering social media ads and podcasts to meet reach goals.
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 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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".