How are Conversations via an On-Demand Peer-To-Peer Emotional Well-Being App Associated with Emotional Improvement?
Bibliographic record
Abstract
Non-clinical, on-demand peer-to-peer (PtP) support apps have become increasingly popular over the past several years. Although not as pervasive as general self-help apps, these PtP support apps are usually free and instantly connect individuals through live texting with a non-clinical volunteer who has been minimally trained to listen and offer support. To date, there is little empirical work that examines whether and how using an on-demand PtP support app improves emotional well-being. Applying regression and multilevel models to N = 1000+ PtP conversations, this study examined whether individuals experience emotional improvement following a conversation on a PtP support app (HearMe) and whether dyadic characteristics of the conversation – specifically, verbal and emotional synchrony – are associated with individuals’ emotional improvement. We found that individuals reported emotional improvement following a conversation on the PtP support app and that verbal (but not emotional) synchrony was associated with the extent of individuals’ emotional improvement. Our results suggest that online PtP support apps are a viable source of help. We discuss cautions and considerations when applying our findings to enhance the delivery of support provision on PtP apps.
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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 | 0.007 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".