Individual Motives for Security Influence Sexual Activity During the COVID-19 Pandemic
Bibliographic record
Abstract
Amidst a global pandemic, people’ survival needs become salient and the ability for people to regulate feeling and actions might be particularly relevant to protecting oneself from harm. Regulatory Focus Theory (Higgins, 1998) proposes that people pursue their goals by having a focus on prevention (i.e., motivated by security) or promotion (i.e., motivated by pleasure). Prior research indicates that people focused on prevention (vs. promotion) are more likely to engage in health-protective behaviors, including sexual health behaviors, because they perceive more threats. Extending this reasoning to the sexual activity of single people during the COVID-19 pandemic, we conducted a pre-registered longitudinal study (N = 174) examining the role of regulatory focus on people’s sexual behaviors. As hypothesized, results showed that single people who reported having a more prevention focus at the onset of the pandemic perceived greater threats caused by the pandemic two weeks later, which, in turn, predicted less frequent sexual activity and engagement in sex with fewer sexual partners the following two weeks. These effects were consistent even when controlling for promotion (i.e., pleasure motivations), personality, gender, and sexual orientation. Findings are discussed considering their implications for the sexual functioning and sexual health of single people.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".