The Role of Distress Disclosure Tendencies in the Experience and Expression of Laboratory-Induced Sadness
Bibliographic record
Abstract
Abstract. The benefits of talking with others about unpleasant emotions have been thoroughly investigated, but individual differences in distress disclosure tendencies have not been adequately integrated within theoretical models of emotion. The purpose of this laboratory research was to determine whether distress disclosure tendencies stem from differences in emotional reactivity or differences in emotion regulation. After completing measures of distress disclosure tendencies, social desirability, and positive and negative affect, 84 participants (74% women) were video recorded while viewing a sadness-inducing film clip. Participants completed post-film measures of affect and were then interviewed about their reactions to the film; these interviews were audio recorded for later coding and computerized text analysis. Distress disclosure tendencies were not predictive of the subjective experience of emotion, but they were positively related to facial expressions of sadness and happiness. Distress disclosure tendencies also predicted judges’ ratings of the verbal disclosure of emotion during the interview, but self-reported disclosure and use of positive and negative emotion words were not associated with distress disclosure tendencies. The authors present implications of this research for integrating individual differences in distress disclosure with models of emotion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".