Long-distance texting: Text messaging is linked with higher relationship satisfaction in long-distance relationships
Bibliographic record
Abstract
Due to the widespread use of smartphones, romantic couples can connect with their partners from virtually anywhere, at any time. Remote communication may be particularly important to long-distance relationships (LDRs), compared to geographically close relationships (GCRs). The goals of the current research were to examine differences between LDRs and GCRs in (1) the patterns of remote communication (video calls, voice calls, and texting), and (2) how frequency and responsiveness of remote communication are related to relationship satisfaction. Data were drawn from an online survey of emerging adults ( n = 647) who were in a relationship or dating someone (36.5% were in an LDR). Participants in LDRs engaged in more frequent video calling, voice calling and texting, compared to those in GCRs. Long-distance relationship participants also perceived their partners to be more responsive during video and voice calls, compared to GCR participants. More frequent and responsive texting predicted significantly greater relationship satisfaction among participants in LDRs, but not GCRs. Meanwhile, frequency of voice calls was associated with greater relationship satisfaction in GCRs, but not in LDRs. The use of video calls was not significantly related to relationship satisfaction in either group. Overall, study findings add to a growing literature on remote communication in romantic couples and suggest a uniquely positive role of texting within LDRs. Further research is needed to examine the ways in which LDR and GCR couples can best capitalize on different forms of remote technology to maintain their relationships during periods of separation.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".