Posttraumatic Stress and Alexithymia
Bibliographic record
Abstract
ABSTRACT: There is a demonstrated association between alexithymia and posttraumatic stress disorder (PTSD). However, work has largely focused on male-dominant, high-risk occupation populations. We aimed to explore the relationship between posttraumatic stress (PTS) and alexithymia among 100 trauma-exposed female university students. Participants completed a Life Events Checklist, the PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PCL-5), and the Toronto Alexithymia Scale (TAS-20). Multiple regressions were run to examine whether alexithymia was associated with each of the PCL-5 subscales. The TAS-20 total scores were associated with total PTS scores, β = 0.47, t(99) = 5.22, p < 0.001. On a subscale level, Difficulty in Identifying Feelings (DIF) was positively associated (β = 0.50 to 0.41) with all PCL-5 subscales except for Avoidance. Our results align with research showing that for women, the DIF subscale is most strongly associated with PTS, in contrast with the literature on male samples, showing strongest associations with the Difficulties in Describing Feelings subscale, suggesting sex differences in associations between PTS and alexithymia. Our study supports the universality of the associations between alexithymia and PTS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".