Comparing Three Distinct Samples on Traumatic Events, Post Traumatic Stress Disorder and Dysfunctional Coping Styles
Bibliographic record
Abstract
The purpose of this study was to compare three distinct United States (US) samples on traumatic events, dysfunctional coping styles and Post Traumatic Stress Disorder (PTSD). The samples were: civilian (n = 97); non-combat military veterans (n=61) and combat military veterans (n = 91). An online survey was used to collect all the data. The average age across all participants was 29 years old. For the overall combined sample, three avoidance coping styles, venting, denial, and dark humor, were each positively related to Post Traumatic Stress Disorder (PTSD). Looking at differences between the three samples, the combat veteran sample had more traumatic events (TEs), with the most recent TE being longer ago, then the non-combat veteran and civilian samples. There were no sample differences in PTSD. However, the non-combat veteran sample had higher levels of denial, venting and dark humor in dealing with their most recent TE, than the other two samples. This research draws needed attention to helping non-combat military veterans cope in a more positive way with their most recent TE. Future research directions and study limitations 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".