Say you’ll be there: Associations between observed verbal responses, friendship quality, and perceptions of support in young adult friendships
Bibliographic record
Abstract
Friendships are a primary source of social support during young adulthood; however, little is known about the factors associated with young adults feeling greater support during interactions with friends. We examined how micro-level verbal responses and macro-level judgments of friendship quality were associated with perceptions of support following an interaction between friends. Same-gender friend dyads ( N = 132; 66.2% female; 18–24 years, M age = 19.63) took turns speaking about a problem, then participants rated their perceptions of support given and received following the task. We coded each participant’s verbal responses while in the listening role. Actor Partner Interdependence Models (APIMs) revealed significant partner effects for negative engagement responses, such that greater negative engagement responses were linked with the partner perceiving poorer support both given and received. Models revealed significant actor effects for supportive responses, such that greater supportive responses predicted the actor perceiving better support both given and received. Additionally, models revealed significant actor effects of friendship quality predicting actors’ perceiving better support both given and received. Finally, exploratory models revealed minimal interactions between a few types of verbal responses and positive friendship quality. Taken together, results suggest that (a) negative verbal responding styles may be more meaningfully associated with partners’ perceptions of support in the moment than are supportive behaviours, whereas (b) supportive verbal responding styles may be more meaningfully associated with actors’ perceptions of support in the moment, and (c) actors’ judgments of friendship quality are strongly associated with their overall perceptions of support, and a critical factor to consider in future research.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".