Disclosure and Social Acknowledgement as Predictors of Recovery from Posttraumatic Stress: A Longitudinal Study in Crime Victims
Bibliographic record
Abstract
OBJECTIVE: To address posttraumatic stress disorder (PTSD) predictors with research focused on the coping styles of traumatized individuals. METHOD: A total of 86 crime victims (mean age 46.1, standard deviation 17.6) were assessed at 5 and 11 months post-crime. Disclosure of trauma, social acknowledgement, dysfunctional posttraumatic cognitions, and PTSD symptom severity were assessed by self-reports. RESULTS: Dysfunctional posttraumatic cognitions, disclosure attitudes, and social disapproval correlated positively with PTSD severity. Hierarchical regression analyses revealed the particular value of disclosure attitudes and perceived social disapproval in predicting PTSD symptom severity at 11 months post-crime. CONCLUSIONS: In addition to known predictors of PTSD, disclosure attitudes and social acknowledgement should also be considered. Future research should focus on broader concepts such as the victim's perception of, and interaction with, their social environment, and on the objective factors of social interaction, in addition to intrapersonal processes of posttraumatic recovery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".