"Technoference" and Implications for Mothers' and Fathers' Couple and Coparenting Relationship Quality
Bibliographic record
Abstract
Technology devices are widely used today, creating opportunities to connect and communicate with distant others while also potentially disrupting communication and interactions between those who are physically present (i.e., technoference or phubbing). These disruptions in couple and coparenting relationships have the potential to negatively impact relationship outcomes. In this two-part study of 182 married/cohabiting couples from the Daily Family Life Project and 239 couples from the Couple Well-Being Project, we examined the role of technoference in couple and coparenting relationship quality and potential gender differences utilizing dyadic data. We found that greater technoference related to greater conflict over technology use, and greater conflict predicted lower relationship satisfaction and poorer perceptions of coparenting quality (Study 1). Using a more diverse sample (Study 2), we again found support for the main pathways tested in our first study, suggesting that results found in Study 1 and in previous work are not artifacts of sampling. As satisfaction, support, and agreement among relationship partners and parents are often critical to relationship health and family cohesion, it is important for couples and families to evaluate, monitor, and be willing to adapt their technology usage patterns so that these patterns do not cause conflict and possibly relationship deterioration over time.
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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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".