PTSD Symptoms and Life Satisfaction of Homicidally Bereaved Individuals: Trial Attendance, Trial-Related Factors, and Perceived Social Support
Bibliographic record
Abstract
Experiencing the homicide of a loved one can lead to considerable posttraumatic stress disorder (PTSD) symptoms and impair life satisfaction. Beyond the loss itself, trial attendance and trial-related factors (perception of justice, distress during the trial, dissatisfaction with the verdict) can further influence the PTSD symptoms and life satisfaction of homicidally bereaved individuals (HBI). However, the relationship between trial attendance, trial-related factors and perceived social support (PSS) have not yet been examined. This study compared the PTSD symptoms and life satisfaction of HBI who attended a trial and those who did not. It also investigated whether trial attendance moderated the relationship between PSS, PTSD symptoms and life satisfaction. Finally, the study explored associations between trial-related factors, PTSD symptoms and life satisfaction among HBI who attended a trial. A total of 149 participants (including 63 HBI who attended a trial and 86 who did not) completed an online questionnaire. Results showed that trial attendance moderated the association between PSS and PTSD symptoms. Trial-related factors accounted for a significant portion of the variance of PTSD symptoms and life satisfaction, beyond the contribution of PSS and time since the homicide.
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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 | 0.003 |
| 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 | 0.001 |
| 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".