Long-term cognitive correlates of exposure to trauma: Evidence from Rwanda.
Bibliographic record
Abstract
Research increasingly shows links between trauma exposure, posttraumatic stress symptoms, and cognitive functioning. We know relatively little about the long-term cognitive correlates of exposure to trauma, especially in civilian populations exposed to war and political violence. OBJECTIVE: Our goal was to examined short-term memory (STM) and executive function 20 years after the 1994 genocide of the Tutsis in Rwanda. We hypothesized that performance on these tasks would be negatively related to trauma exposure and to posttraumatic stress disorder (PTSD) symptoms. METHOD: In two studies, 470 Rwandan participants answered questionnaires measuring the severity of experiences that occurred during the 1994 genocide and current levels of PTSD symptoms. In both studies, we tested STM using a forward digit span task. In the second study, we also measured executive function using a semantic fluency task. RESULTS: There were modest but significant negative links between the severity of experiences during the genocide and STM function more than 20 years after. Current levels of PTSD symptoms were also related to STM and executive function. CONCLUSIONS: This study reveals the important link between exposure to highly emotional events and cognitive function and highlights the need to attend not only to the mental health but also to the cognitive health of populations exposed to political violence. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".