Deeper insights into the construct of sexual communication: Understanding of the role of individual and relationship factors and the development of a process model
Bibliographic record
Abstract
Successful and fulfilling intimate relationships are an integral part of overall wellbeing, and their success and meaning are determined by many factors, including sexual communication within the relationship (Byers & Demmons, 1999; Rehman et al., 2011). Despite the substantial body of research that explains the impact of sexual communication on relational and sexual functioning, less is known about the process of communicating sexual topics. Sexual communication is a complex process involving the capacity for vulnerability as well as communication and perspective-taking skills (e.g., Buluş et al., 2017). Theoretical models have important utility in both research and clinical practice, but there are few models of sexual communication that capture its multidimensional nature. The goal of the current program of research was to expand on the foundational work by Brown and Weigel (2018) to develop the Process Model of Sexual Communication (PMSC). This model explains the relationships between individual factors, relationship context factors, and the process of sexual communication and their collective impact on outcome variables such as sexual satisfaction. \n \nI conducted two separate two-part studies (total n = 373) to explore the relationships between these factors and develop the PMSC using quantitative (Studies 1a and 1b) and mixed-method (Studies 2a and 2b) approaches. The results support the multidimensional nature of sexual communication and highlight the different topics that individuals discuss with their partners. They also highlight the importance of motivations for engaging in sexual communication and how those motivations impact the other components of the model. Using these results, I constructed the PMSC and tested associations between the model components. I discuss implications for research and clinical practice as well as future directions to test the full PMSC.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".