Comparing Two Distinct Miliary Samples on Traumatic Events, Positive Coping Styles and Post Traumatic Growth
Bibliographic record
Abstract
This study collected complete data traumatic event-related information, positive coping styles, and post traumatic growth variables from two different United States (US) military veteran samples: non-combat military veterans (n = 54) and combat military veterans (n = 84). Although both samples represent military veterans, only one sample experienced actual combat, i.e., active fighting in a war against an enemy. All data were collected via online survey. Demographically, both samples were predominately White male, with a four-year college degree being the highest education level frequency. The average participant age was 29 years and there was no significant mean age difference between the samples. In addition, there were no significant sample differences in the total number of traumatic events experienced or time since the most powerful traumatic event was experienced. The purposes of this study were to: (1) test if four positive coping strategies were related to Post Traumatic Growh (PTG), and (2) to determine if there were differences in the use of these four coping strategies or experienced PTG for non-combat versus combat military veterans. The four positive coping styles were measured, instrumental support, emotional support, religion, and acceptance. For the combined sample, two coping styles, instrumental support and religion were each significant positively related to post traumatic growth (PTG). Significant sample differences were found on instrumental support and religion such that the non-combat veterans perceived higher mean levels on both coping styles versus the combat veterans. No sample difference was found for PTG. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".