Exploring Pathways from Growth to Wisdom: The Role of Coping Styles and Meaning-Making
Bibliographic record
Abstract
Variations in cognition and coping play a key role in the maintenance of post-traumatic stress disorder (PTSD), therefore it is important to know how individuals can successfully cope with trauma. Self-compassion (SC) and meaning may help an individual grow in a transformative manner following trauma. Moreover, recent research has shown a link between SC, posttraumatic growth (PTG), and emotion-focused coping strategies. Studies have also found evidence for the use of problem-focused strategies in predicting PTG. Therefore, this study had three main objectives: (1) investigate the association of negative cognitions (about the self, the world, and self-blame), coping, and their interactions with PTSD, (2) examine whether SC and meaning are associated with PTG in individuals with probable PTSD, and (3) investigate the association between SC, emotion- and problem-focused coping, and PTG. Participants were 111 emerging adults who participated in an online survey. First, a hierarchical multiple linear regression was conducted to test the hypothesis related to coping, negative cognitions, and PTSD. At step 1, self-distraction and substance use were associated with PTSD. At step 2, these coping styles and negative cognitions about the world were associated with PTSD. In step 3, no interactions were significant. Second, for the probable PTSD group, SC was associated with meaning and PTG, whereas SC and PTG were not correlated in the non-PTSD group. There was an indirect effect of SC on PTG via meaning in those with probable PTSD. Lastly, SC and PTG were correlated with active coping, instrumental support, and positive reframing. All three coping styles were associated with PTG over and above SC. Findings indicate that distracting oneself, using substances, and maintaining a negative view of the world significantly impacts PTSD. Trauma may require individuals to redefine negative beliefs to rebuild their shattered assumptive worlds. SC also involves a caring about one’s life that affords a greater opportunity for PTG. SC-based interventions could be beneficial in supporting growth after adversity. Problem-focused coping strategies are also influential in mediating the development of PTG from SC. Incorporating problem-focused coping into SC-based practices in trauma therapies may offer additional benefits by reducing self-criticism to promote recovery.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".