Event-related Factors, Altruism, and Substance Use in Traumatization of Hurricane Student Volunteers: A Bayesian Model for the Follow-up Running Head: Bayesian Analysis of Disaster Traumatization
Bibliographic record
Abstract
In surging disaster research, trauma psychologists called for more longitudinal investigation on factors related to resilience/lower traumatization for populations exposed to collective trauma. Little research has employed a Bayesian approach, a means with advantages in small samples and dichotomized endpoints. The present study addressed these needs with a two-wave survey on hurricane volunteers to demonstrate pathways to traumatization after deadly disasters. A survey was conducted at three months (Wave-1) and six months (Wave-2) after hurricane Katrina and Rita (H-KR) (N=201). Standardized instruments were used to assess posttraumatic stress symptoms (PTSS) altruism, substance use for coping, and event-related factors in Wave-1 and posttraumatic stress disorder (PTSD) in Wave-2. Bayesian structural equation modeling (Bayesian-SEM) was performed to evaluate the role of altruism and using substances to cope with Wave-2 PTSD. Traumatization was identified in 18% of participants, showing a significant increase in Wave-1 and a 12% decrease, albeit non-significant, in Wave-2. Supported by all Model fit indices, the final solution of Bayesian-SEM showed no direct overtime effect of altruism and substance use, but the indirect effects through the enhancing role of Waves-1 PTSS, on Wave-2 PTSD. Contrary to cross-sectional studies, no protection from peritraumatic positive emotions was observed. These findings emphasize the importance of longitudinal post-disaster research. Given the new evidence on volunteers' traumatization, altruism, and substance use during times of crisis with limited resources, further investigation among volunteers is crucial. The absence of identified protective factors in volunteers raises concerns for future implications in trauma psychology theory, research, and practice.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".