Romance Behind the Screens: Exploring the Role of Technoference on Intimacy
Bibliographic record
Abstract
Intimacy is essential for fulfilling romantic relationships. Although many factors can impact intimacy, the increased regular use of technological devices within our daily lives makes technoference an important one to consider. Technoference (i.e., interference in face-to-face interactions caused by the use of technological devices) is commonly associated with relationship difficulties, including conflict, dissatisfaction, and decreased relational well-being. However, less is known about the direct and indirect impact of technoference on intimacy among couples. We hypothesized that negative perceptions of a partner's technology use and poor communication satisfaction within a romantic relationship help explain the association between technoference and intimacy. University students (N = 141), who were in a romantic relationship of at least 6 months duration, completed online questionnaires assessing technoference, perceptions of their partner's technology use, communication satisfaction, and intimacy in their romantic relationship. PROCESS macro model 6 was used to test the serial mediation models. Results suggest that the relationship between technoference (general, partner's, and participant's) and intimacy is serially mediated by negative perceptions of partner's technology use and communication satisfaction. These findings can help to identify and inform strategies to maximize intimacy levels between couples, thus fortifying romantic relationships as a whole.
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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 | 0.003 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".