Using self-compassion to grow in the face of trauma: The role of positive reframing and problem-focused coping strategies.
Bibliographic record
Abstract
OBJECTIVE: Recent research has shown a link between self-compassion, posttraumatic growth (PTG), and emotion-focused coping strategies (i.e., positive reframing and acceptance). Studies have also found evidence for the use of problem-focused strategies (i.e., active coping, planning, and instrumental support) as mediators between self-compassion and stress, and the use of these strategies has been found to predict PTG. However, no studies have directly examined the relationship between self-compassion, PTG, and the use of problem-focused coping strategies. This study investigated the association between self-compassion, emotion- and problem-focused coping, and PTG in trauma survivors. METHOD: Participants were 111 emerging adults aged 18 to 29, from Canada and the United States, who completed an online survey that included measures of coping, PTG, and self-compassion. RESULTS: Self-compassion and PTG were both correlated with three coping styles, active coping, instrumental support, and positive reframing. All three coping styles predicted PTG over and above self-compassion and played multiple mediating roles between self-compassion and PTG, with no differences between the three coping styles in their mediating effects. CONCLUSIONS: These findings indicate that problem-focused coping strategies are also influential in mediating the development of PTG from self-compassion. Self-compassion reduces one's tendency to overidentify with negative emotions through positive reframing. The use of active coping and instrumental support also allows individuals to feel more capable in dealing with their traumatic events. Incorporating problem-focused self-compassion-based practices in cognitive behavioral and exposure-based therapies may offer additional benefits by reducing self-criticism to better promote active recovery from traumatic events. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".