Understanding motivations for sexual communication from a regulatory focus perspective
Bibliographic record
Abstract
Despite the overwhelming support for the importance of sexual communication to intimate relationships, there is limited information about what motivates someone to engage in or avoid sexual communication. Motivational frameworks have been applied to various aspects of intimate relationships, serving as strong predictors of different behavioural processes and playing a crucial role in facilitating behavioural change. As such, we aimed to elucidate the motivations for sexual communication and explore how they relate to other aspects of the process of sexual communication. A total of 373 participants were recruited from online crowdsourcing websites across two studies, and they completed online questionnaires using a mixed methods approach. In Study 1, open-ended responses regarding participants' motivations for sexual communication were inductively coded and aligned with the Regulatory Focus Theory, which describes two distinct modes of goal pursuit depending on if the person is focused on growth and advancement (i.e., promotion-focused) or safety and security (prevention-focused). This coding structure was replicated in Study 2, and we expanded the results to examine the predictive ability of the coded motivations. We found that those higher in attachment avoidance were more likely to have prevention-focused motivations, and those with relationship-oriented promotion-focused motivations reported more depth of both sexual and nonsexual communication as well as more relationship and sexual satisfaction. The implications of these findings are discussed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".