Examining Correlates of Problematic Internet Pornography Use Among University Students
Bibliographic record
Abstract
Background and aims The phenomenon of Internet pornography (IP) addiction is gainingincreasing attention in the popular media and psychological research.What has not been tested empirically is how frequency and amount ofIP use, along with other individual characteristics, are related tosymptoms of IP addiction. Methods 105 female and 86 male university students (mean age 21) from Calgary,Canada, were administered measures of IP use, psychosocial functioning(anxiety and depression, life and relationship satisfaction), addictivepropensities, and addictive IP use. Results Men reported earlier age of exposure and more frequent currentIP use than women. Individuals not in relationships reported morefrequent use than those in relationships. Frequency of IP use wasnot generally correlated with psychosocial functioning but was significantlypositively correlated with level of IP addiction. Higher level ofIP addiction was associated with poorer psychosocial functioning andproblematic alcohol, cannabis, gambling and, in particular, videogame use. A curvilinear association was found between frequency ofIP use and level of addiction such that daily or greater IP use wasassociated with a sharp rise in addictive IP scores. Discussion The failure to find a strong significant relationship between IPuse and general psychosocial functioning suggests that the overalleffect of IP use is not necessarily harmful in and of itself. Addictiveuse of IP, which is associated with poorer psychosocial functioning,emerges when people begin to use IP daily.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".