Peritraumatic dissociation and post-traumatic stress disorder in individuals exposed to armed conflict in the Democratic Republic of Congo
Bibliographic record
Abstract
The purpose of this study was to verify the hypothesis that there is an association between peritraumatic dissociation (PD) and post-traumatic stress disorder (PTSD) in individuals exposed to recurrent armed conflict. More specifically, we sought to evaluate whether PD differentially predicts PTSD according to the degree of exposure to the potentially traumatic event (PTE), the level of education, and gender. A total of 120 individuals between 17 and 75 years of age, including 51 women, completed the Traumatic Events List, the Peritraumatic Dissociative Experiences Questionnaire, and the French version of the Posttraumatic Stress Disorder Checklist Scale, as well as a questionnaire providing information regarding sociodemographic details. The group of participants with high scores for PD had significantly more PTSD. PD differentially predicts PTSD depending on the level of education and gender of the individual. Those who had been physically assaulted and raped, as well as the less educated, were more likely to be dissociated during PTE· exposure compared to witnesses and those with a higher level of education. The primary target population for prevention and early management should comprise individuals with high levels of PD, low levels of education, and women.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".