Multilingualism and Exposure to Traumatic Memories
Bibliographic record
Abstract
This study examined how and to what extent secondary language effected level of exposure to, and experience of, an American or Canadian multilingual individual’s traumatic memory. Thirteen American and Canadian multilingual individuals with trauma histories participated in this online study that consisted of a screening questionnaire, the PTSD Checklist for DSM-5 (PCL-5), the Language Experience and Proficiency Questionnaire (LEAP-Q), and the short form version of the Memory Experiences Questionnaire (MEQ-SF), with this final measure being repeated twice. Findings from paired t-tests indicated that participants in this study did not report differences in how they experienced their trauma memory when it was recounted using their primary or secondary language. Participants with probable moderate posttraumatic stress disorder (PTSD) demonstrated an increase in the phenomenological dimensions of Visual Perspective and Sharing when they recounted their trauma memory using their secondary language, while participants with probable severe PTSD demonstrated decreases in the phenomenological dimensions of Visual Perspective and Accessibility when they recounted the trauma memory using their secondary language. Participants with probable high symptoms of avoidance did not experience differences in how their trauma memory was experienced when it was recounted in their primary or secondary language. Findings from this study suggest that how a traumatic memory is experienced by a multilingual individual with trauma history may depend on the severity of their PTSD. It is recommended that further research be conducted on this topic due to the limitations of this study.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".