Comparing posttraumatic stress disorder's symptom structure between deployed and nondeployed veterans.
Bibliographic record
Abstract
We tested two empirically validated 4-factor models of posttraumatic stress disorder (PTSD) symptoms using the PTSD Checklist: King, Leskin, King, and Weathers' (1998) model including reexperiencing, avoidance, emotional numbing, and hyperarousal factors, and Simms, Watson, and Doebbeling's (2002) model including reexperiencing, avoidance, dysphoria, and hyperarousal. Our aim was to determine which fit better in two groups of military veterans: peacekeepers previously deployed to a war zone (deployed group) and those trained for peacekeeping operations who were not deployed (nondeployed group). We compared the groups using multigroup confirmatory factor analysis. Adequate model fit was demonstrated among the nondeployed group, with no significant difference between King et al.'s (1998) model (separating avoidance and numbing) and Simms et al.'s (2002) similar model involving a dysphoria factor. A better fitting factor structure consistent with Simms et al.'s (2002) model was found in the deployed group. Comprehensive measurement invariance testing demonstrated significant differences between the deployed and nondeployed groups on all structural parameters, except observed variable intercepts (thus indicating similarities only in PTSD item severity). These findings add to researchers' understanding of PTSD's factor structure, given the revision of PTSD that will appear in the forthcoming 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2010)--namely, that the factor structure may be quite different between groups with and without exposure to major traumatic events.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".