Busting MisconSEXions: evaluation of a social media knowledge translation initiative addressing myths about sex
Bibliographic record
Abstract
There is a critical gap in sex education such that many people lack access to evidence-based and accessible information about sexuality, putting them at risk for endorsing myths about sex and in turn having poorer sexual wellbeing. To address this gap, we developed a novel social media knowledge translation initiative—MisconSEXions—to debunk common myths about sexuality. The goal of this study was twofold. First, to examine whether exposure to MisconSEXions is effective for reducing sexuality myth endorsement. Second, to evaluate the acceptability (participants’ satisfaction with the content), appropriateness (the perceived fit of the content with participants), adoption (participants’ intention to engage with the initiative), and penetration (participants’ perception of the content’s impact on their lives) of MisconSEXions among study participants. We also examined possible group differences in our observed effects by assigned sex, gender modality, and sexual orientation. A large and diverse sample (N = 2,356) of adults completed an online survey and reported on their demographics, sexuality myth endorsement before and after exposure to MisconSEXions content, and the acceptability, appropriateness, adoption, and penetration of the MisconSEXions content. We found that participants’ sexuality myth endorsement was significantly lower following exposure to MisconSEXions content, and this effect held across assigned sex, gender modality, and sexual orientation groups. Regardless of participants’ assigned sex, gender modality, or sexual orientation, MisconSEXions content was reported to be both acceptable and appropriate to people’s lives. Participants reported relatively low levels of adoption, such that they reported reluctance to engage with the content on social media. Additionally, participants reported mixed feelings regarding the impact of the content on their lives (i.e., penetration). Overall, the findings have implications for how sexuality social media knowledge translation initiatives can fill important gaps in providing inclusive and accessible sex education.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".