The Mediating Role of Alexithymia in the Association Between Adverse Childhood Experiences and Postdeployment Mental Health in Canadian Armed Forces Personnel
Bibliographic record
Abstract
Abstract Recent studies showing an association between adverse childhood experiences and the development of alexithymia in military personnel have generated interest regarding the role of alexithymia in the pathway linking childhood trauma exposure to mental health disorders. Accordingly, the current study was conducted to (a) examine the associations among adverse childhood experiences, alexithymia, and symptoms of depression and posttraumatic stress disorder (PTSD) among recently deployed Canadian military personnel and (b) assess the mediating role of alexithymia in these associations. Data collected from 2,927 members of the Canadian Armed Forces at baseline and after their return from an overseas deployment were subjected to a prospective path analysis. The results of the path analysis, R2 = .35, pointed to a significant direct effect of childhood adversity on postdeployment mental health symptoms. Contrary to our expectations, the results also pointed to a negative indirect effect of childhood neglect, suggesting that childhood neglect contributed to lower levels of postdeployment depression and PTSD symptoms through the dimension of alexithymia related to difficulty in describing feelings. These patterns of associations, for the most part, persisted even when accounting for combat exposure during recent deployments, R2 = .42. The present results are discussed in light of study limitations and methodological considerations, and policy and clinical implications are noted.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".