Understanding Discussions of Sexual Assault in Young Women on a Peer Support Mental Health App: A Content Analysis
Bibliographic record
Abstract
Trauma narratives may have been influenced by the Me Too movement, with thousands of individuals disclosing sexual violence stories online. Youth, the largest demographic of online users, may prefer the anonymity of the Internet to discuss experiences of sexual assault. Understanding the ways that young women, especially those experiencing mental health difficulties, discuss their experiences is important as they are at higher risk of revictimization and continued poor mental health. We searched for terms related to acts of sexual assault on a mental health peer-support app, TalkLife, and compared the number of posts during the initial wave of the Me Too movement (October 2017–March 2018) to the same time period in the previous year (October 2016–March 2017). We found a significant increase in posts related to sexual assault of 49.7% between the Pre and Post Me Too time periods ( p < .001), controlling for a general increase in posts. A content analysis of 700 randomly selected posts found that a substantial number of young women used TalkLife to discuss their experiences of sexual assault, and these self-disclosures were mostly hopeless or depressing in tone. Additionally, neither the nature nor the number of self-disclosures varied across time points. The negative tone of the self-disclosures in the current study is worrying because the way women talk about their trauma can shape how they understand it, which could lead to negative self-appraisal and continued mental health difficulties. Online spaces have the potential to support young women and facilitate help-seeking, but we must be attentive to how they are used.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".