Are personnel with a past history of mental disorders disproportionately vulnerable to the effects of deployment-related trauma? A cross-sectional study of Canadian military personnel
Bibliographic record
Abstract
BACKGROUND: Past mental disorders predict future disorders, both in the presence and absence of trauma exposure. However, it is not clear whether those with past mental disorders are disproportionately vulnerable to the negative effects of a given level of trauma. METHODS: The data source was the 2013 Canadian Forces Mental Health Survey (CFMHS), of which 1820 respondents had deployed only once in their military careers-all in support of the mission in Afghanistan. The primary outcomes were past 12-month depression and past 12-month PTSD. Multivariate logistic regression was performed for each outcome variable, looking primarily for differences in the marginal effect of deployment-related trauma in those with and without a pre-deployment history of each disorder. RESULTS: A history of each pre-deployment disorder did indeed interact with deployment-related trauma with respect to the corresponding past 12-month disorder. In addition, pre-deployment history of depression and of PTSD interacted with each other, though only for the outcome of past 12-month PTSD. The average marginal effect of deployment-related trauma on past 12-month PTSD was highest in those with a pre-deployment history of depression in the absence of a pre-deployment history of PTSD. This group was twice as vulnerable to post-deployment PTSD relative to those without a pre-deployment history of both disorders and four times as vulnerable to post-deployment PTSD relative to those with a pre-deployment history of both disorders. No significant differences were seen in the marginal effects of trauma on past 12-month depression in the presence or absence of a pre-deployment history of that disorder. CONCLUSION: There is modest differential vulnerability to past 12-month PTSD as a function of deployment-related trauma in those who had a pre-deployment history of PTSD or depression when compared to those who did and did not have a pre-deployment history of one or both disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".