Post-Traumatic Stress Disorder Symptoms in United Nations Peacekeepers: An Examination of Factor Structure in Peacekeepers with and without Chronic Pain
Bibliographic record
Abstract
Recent factor analytic investigations of post-traumatic stress disorder in military veterans suggest that symptoms are best described by either a hierarchical 2-factor model or a 4-factor inter-correlated model. Other recent evidence suggests that post-traumatic stress disorder and chronic pain are intricately related; however, the nature of this relationship is not well understood. Factor analysis provides one method for clarifying this relationship. In study 1, we compared competing models of post-traumatic stress disorder symptom structure in a sample of 400 male United Nations peacekeepers using confirmatory factor analysis. Results indicated that both the hierarchical 2-factor and the 4-factor inter-correlated models provided good fit to the data. In study 2, the reliability of these models was assessed in 427 male United Nations peacekeepers with chronic back pain and 341 without. Group comparisons of the confirmatory factor analysis results revealed that the structure of the hierarchical 2-factor and 4-factor inter-correlated models both provided good fit to the data in both the chronic back pain and the group without. However, the structure of the models for the group with chronic back pain group differed in significant ways from that of the group without chronic back pain. Post-traumatic stress disorder symptoms in military veterans can be adequately conceptualized using either a hierarchical 2-factor or 4-factor inter-correlated model. Chronic pain has a minimal influence on overall factor structure. The hierarchical 2-factor model, while parsimonious, does not provide the degree of symptom detail provided by the 4-factor inter-correlated model. Implications for conceptualization of post-traumatic stress disorder symptoms for patients with chronic back pain and significant post-traumatic stress disorder symptomatology are discussed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".