Prosocial reactions to traumatic experiences
Bibliographic record
Abstract
When will people empathize with and help others? The goal of this research was to determine whether a prosocial orientation results from experiencing trauma. Recent research suggests there may be positive consequences to suffering. Under certain conditions, such as when people experience post-traumatic growth, past suffering can lead to personal benefits. Building on this body of research, one aim of this thesis was to investigate the impact of subjective traumatic suffering and psychological distress on post-traumatic growth and empathy. The second aim of this research was to examine whether objective trauma severity predicts post-traumatic growth. Finally, the third aim of this research was to examine the relationship between post-traumatic growth and empathy and the simultaneous impact of these variables on a prosocial orientation. Study 1 assessed these aforementioned relationships and Study 2 included a manipulation of post-traumatic growth and a behavioural outcome measure of prosocial behaviour. Structural equation models for Study 1 and 2 indicated that subjective traumatic suffering and objective trauma severity positively predicted post-traumatic growth, and post-traumatic growth positively predicted empathy. In turn, empathy positively predicted several prosocial outcomes. Thus, empathy mediated the link between post-traumatic growth and a prosocial orientation. In contrast to subjective traumatic suffering, psychological distress was unrelated to post-traumatic growth and negatively predicted empathy. Study 2 further indicated that focusing on one’s growth in regards to trauma resulted in greater post-traumatic growth scores, but the manipulation had no direct impact on empathy or a prosocial orientation. The current findings have important social and clinical implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".