Young Adults’ Qualitative Self-Reports of Their Outcomes of Online Sexual Activities
Bibliographic record
Abstract
Online sexual activities (OSA) refer to Internet-based activities, behaviours, and materials that are sexual in nature. Many young adults engage in OSA, but report doing so infrequently. Most OSA outcome research has focused on negative effects of only some types of OSA (e.g., viewing pornography online). The goal of this study was to enhance knowledge on the range of OSA outcomes by qualitatively exploring young adults’ self-reported negative and positive outcomes from OSA experiences generally. University/College students from Canada (n = 246), Germany (n = 411), Sweden (n = 299), and the USA (n = 123) completed an online survey that included open-ended questions about “one of the most positive/negative effects that engaging in online sexual activities has had on your life”. More participants provided positive outcome responses than negative outcome responses. Qualitative analysis of the responses suggested a wide range of positive and negative outcome content that fit into seven bi-polar, higher-order themes: No Outcomes, Relationship Outcomes, Sexual Experience, Emotional Outcomes, Knowledge, Personal Outcomes, and Security. We found no variations in themes or their respective codes across the four countries. The findings suggests that researchers, educators, health care and psychology providers need to include multiple dimensions of positive and negative, personal and interpersonal, sexual and non-sexual OSA outcomes in their work
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".