Curves and Pixels: Longitudinal Associations Between Frequency of Pornography Use and Body Dissatisfaction in a Sample of Young Hungarian Adults
Bibliographic record
Abstract
Abstract Introduction Despite unrealistic body portrayals commonly seen in pornographic materials, only a few studies have examined the association between pornography use frequency (PUF) and body dissatisfaction (BD) over time and most of these studies have been limited in scope (e.g., only focused on women) and included several limitations (e.g., cross-sectional designs). The present study sought to address these gaps by investigating the associations between PUF and BD over a one-year period, while also considering gender-related differences. Method We used an autoregressive cross-lagged analysis with a multi-group approach among 3,733 young adults ( M age = 23, SD age = 4.74, 48.2% men and 51.8% women). Data for the first wave were collected between March and July 2019, and for the second wave between June and September 2020. Results Findings showed that higher levels of PUF were cross-sectionally associated with higher levels of BD among men and women as well. Longitudinally, a bidirectional association was present between PUF and BD in men but not in women. Men’s higher levels of PUF at baseline were associated with greater BD one year later, and higher levels of BD at baseline were associated with increased PUF one year later. Conclusions Findings indicate that pornography use is positively linked to BD in men both short- and longer-term, but only in short-term among women. Adults who consume pornography might be influenced by the unrealistic and idealized body portrayals, resulting in body concerns. Policy Implications Mental health professionals should consider pornography consumption when treating individuals experiencing body dissatisfaction. Policymakers can integrate media literacy education that addresses the unrealistic expectations fostered by pornography into their sexual education curricula.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".