Positive Change or Just “Bouncing Back”?: Resilience & Posttraumatic Growth After Military Adversity
Bibliographic record
Abstract
Recovery in the face of adversity is a crucial process to understand in the context of high-stress occupations such as the military. While resilience has been a central topic of consideration across disciplines, comparatively less work has been done to examine opportunities for positive change after adversity such as posttraumatic growth (PTG). Moreover, the central mechanisms through which individuals can avoid negative outcomes, recover, and experience growth in the aftermath of adversity are important to understand if leaders and practitioners aim to promote the well-being of workers. In this work we draw from theories of resilience and PTG to work toward the development of an integrative model of recovery. We utilize a sample of military personnel who experienced a highly stressful or traumatic event during their time of service in order to examine recovery experiences in the context of military work-related exposures. The results ultimately highlight the direct importance of social support on the experience of PTG, as well as in strengthening the relation between personal characteristics and self-regulatory processes of resilience. Additionally, affective self-regulatory processes were uncovered as a link between personal characteristics of resilience and PTG. Implications for future research and practice 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".