“You’re Not Alone”: How Adolescents Share Dysmenorrhea Experiences Through Vlogs
Bibliographic record
Abstract
Many adolescents experience severe pain during menstruation, yet their attempts to receive medical attention to alleviate or manage this pain are often met with dismissal or disbelief. In light of these barriers to care, many adolescents turn to social media to share their experiences with menstruation and pain, as well as hear from other members of their community. In this study, we investigated how adolescents present their experiences with menstruation in vlogs (or “video blogs”). Using critical qualitative methods and a four-column analysis structure, we transcribed and thematically analyzed the audio and video content of 17 YouTube vlogs wherein adolescents described their experiences with menstrual pain. We found that stylistically, the vloggers modulated between a polished documentary style and an intimate storytime style of video production. We additionally found that vloggers spoke about their menstrual pain experiences from three perspectives: as a Patient managing and diagnosing physical symptoms, as a Self considering how the pain affects their life and ambitions, and as a Teacher educating their audience. Considering both the visual and audio data, we discuss how healthcare providers can use these findings to inform their approach to discussing menstrual pain with adolescents. We further discuss possible future directions for research into health story sharing on social media.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".