Seeking Connection Versus Avoiding Disappointment: An Experimental Manipulation of Approach and Avoidance Sexual Goals and the Implications for Desire and Satisfaction
Bibliographic record
Abstract
Previous correlational research has demonstrated an association between people's reasons for having sex (i.e., their sexual goals) and their sexual desire and sexual and relationship satisfaction. Across two studies of people in romantic relationships (N = 396) we extend previous research and demonstrate, for the first time, that manipulating the salience of approach sexual goals (i.e., engaging in sex to pursue positive outcomes, such as enhanced intimacy) compared to avoidance sexual goals (i.e., engaging in sex to avert negative outcomes, such as a partner's disappointment) or a control condition leads people to feel higher sexual desire for their romantic partners and to report higher sexual and relationship satisfaction. In addition, in Study 2 we demonstrate that focusing on approach sexual goals over the course of a week leads people to report more satisfying sexual experiences during that week, as well as higher desire and overall relationship satisfaction, compared to a control group. The current findings advance approach-avoidance theory by providing evidence that it is possible to manipulate people's sexual goals and, in turn, impact their feelings of desire and satisfaction. Results are promising for the development of interventions to promote sexual and relational well-being.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".