No Nut November, temporary abstinence, and sexual wellbeing: a study of the short-term abstinence-based internet trend
Bibliographic record
Abstract
BACKGROUND: No Nut November (NNN) is a mainstream Internet challenge wherein participants attempt to avoid masturbation, or ejaculation as a result of masturbation, for the month of November. AIM: We investigated the trend and its participants, as well as the sexual outcomes of participating in NNN in a diverse sample. METHODS: This online survey study collected detailed sociodemographic information and data on sexual wellbeing at 2 time points, before and after NNN from those who do and do not engage in NNN. Data from 435 individuals at T1 and 114 individuals at T2 were analyzed via descriptives, ANCOVAs, and repeated measure ANOVAs. OUTCOMES: Main outcome measures were Sexual Pleasure Scale for sexual pleasure, Sexual Desire Inventory-Solitary subscale for solitary desire, Arizona Sexual Experience Scale for sexual dysfunction, SexFlex Scale for sexual flexibility, and Sexual Excitation Scale-SF for sexual excitation. RESULTS: Compared to those who had never participated in the trend, NNN participants reported increased sexual flexibility; however, over a period of abstinence, no measures of sexual wellbeing significantly differed in NNN participants compared to non-participants. CLINICAL IMPLICATIONS: Sexual flexibility may be associated with a willingness to attempt different trends and sexual practices. A period of abstinence may not be associated with improved or worsening sexual wellbeing. STRENGTHS AND LIMITATIONS: Although the present study provides the first investigation of NNN and results indicate no impact after a period of abstinence on sexual wellbeing outcomes, more longitudinal research examining outcomes of abstinence is needed. CONCLUSIONS: These findings may have important implications regarding current practices related to NNN and the increasing role of social media challenges on people's sexual behavior.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".