Interactive relationship between alexithymia, psychological distress and posttraumatic stress disorder symptomology across time
Bibliographic record
Abstract
Alexithymia, psychological distress, and posttraumatic stress disorder (PTSD) are highly related constructs. The ongoing debate about the nature and relationship between these constructs is perpetuated by an overreliance on cross-sectional research. We examined the longitudinal interactive relationship between alexithymia, psychological distress, and PTSD. We hypothesised that there is an interactive relationship between the three constructs. Military personnel (N = 1871) completed the Toronto Alexithymia Scale, the Kessler 10 and a PTSD Checklist (PCL-C) at pre-deployment, post-deployment, and at 3–4 years following the post-deployment assessment. We initially tested whether psychological distress is either a moderator or mediator in the relationship between alexithymia and PTSD across the time points. General psychological distress was a partial mediator of total PTSD severity and hyperarousal symptomology at all three time points. Psychological distress fully mediated re-experiencing and avoidance symptomology at all three time points. Our results suggest that those with alexithymia are at longitudinal risk of developing more severe PTSD symptomology and experiencing hyperarousal irrespective of temporal proximity to traumatic exposure. Further, vulnerability to the emergence of re-experiencing and avoidance symptomology for those with alexithymia is increased when one experiences greater distress. Our results show that alexithymia is a persistent risk factor for PTSD symptomology.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| 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".