Feeling pressured to talk about trauma: How pressure to disclose alters the association between trauma disclosure and posttraumatic growth
Bibliographic record
Abstract
Abstract Talking with others about traumatic experiences (i.e., trauma disclosure) has been associated with increased posttraumatic growth (PTG). Although this association indicates the value of disclosing, there is evidence that external pressure to disclose can hinder the benefits of trauma disclosure. The aim of the current study was to examine the influence of pressure to disclose on the association between trauma disclosure and PTG. People who had experienced a traumatic event and disclosed their trauma to a close other were recruited using Amazon's Mechanical Turk (N = 208). Participants completed measures of trauma exposure, trauma disclosure, pressure to disclose, PTG, posttraumatic stress symptoms, and response to disclosure. The results indicated that the linear association between trauma disclosure and PTG was quadratically moderated by pressure to disclose, ηp2 = .025. Pressure to disclose strengthened the positive association between trauma disclosure and PTG from low, B = 0.818 (SE = 0.267), to moderate levels of pressure, B = 2.109 (SE = 0.471). However, when pressure was high, the association between disclosure and PTG was not significant, B = −1.19 (SE = 1.327). These findings indicate that a moderate amount of pressure to disclose may facilitate the positive impact of disclosure on PTG, yet a high amount of pressure may impede the positive association between disclosure and PTG. This research furthers understanding of the nuances of trauma disclosure and how close others’ involvement in disclosure can impact the process of PTG for trauma survivors.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".