Support at Our Fingertips: An Experimental Comparison of In-Person, Video, Voice and Text-Based Support
Bibliographic record
Abstract
Digital forms of communication afford users unprecedented access to supportive others during times of need. Yet there has been little experimental research that compares the nature and effectiveness of informal support provided through digital communication. In this lab-based experiment, 348 female young adults took part in a stressful task and were randomly assigned to receive support from a close female friend through (1) in-person communication, (2) video calling, (3) voice calling, (4) text messaging, or (5) a no-support control condition. In-person, video and voice communication resulted in similar perceived levels of received support, satisfaction with support, and affective outcomes of support. However, participants who received support through texting reported significantly lower positive affect and less laughter and smiling (compared to all other forms of communication). Text message support was also perceived as less empathetic and resulted in lower satisfaction (compared to in-person communication). The present study replicates and extends past research by identifying specific ways in which text-based support may fall short. In both research and clinical contexts, more work is needed to optimize this popular and convenient platform for the provision of social support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".