Association Between Depression Symptoms and Emotional-Communication Dynamics
Bibliographic record
Abstract
Communicating emotional experiences effectively is critical for adaptive functioning and personal and interpersonal well-being. Here, we investigated whether variability in depression symptoms undermines people’s ability to express their emotions to others (“emotional expressive accuracy”) and how those communication dynamics influence other’s impressions. In Phase 1, 49 “targets” were videotaped describing significant autobiographical events; they then watched their videos and continuously rated how positive/negative they were feeling throughout the narrative. In Phase 2, 171 “perceivers” watched subsets of videos from targets and similarly rated each target’s affect. Results from 1,645 unique target–perceiver observations indicate a link between target’s depressive symptoms and impaired emotional expressive accuracy for positive events, B = −0.002, t (1,501) = −3.152, p = .002. Likewise, more depressive targets were rated less favorably by perceivers, again when sharing positive events, B = −0.012, t (1,511) = −10.145, p < .001. Given the beneficial effects of “capitalization”—sharing positive experiences with others—these findings may illustrate one link between depressive symptoms and impoverished relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".