Posttraumatic Growth Following Suicide Bereavement: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Posttraumatic growth (PTG) is personal growth which occurs due to experiencing a traumatic or extremely challenging event or crisis. As this review is an update, we aim to perform a contemporary search for demographic characteristics, correlational relationships, and facilitating as well as impeding factors of PTG in suicide bereaved individuals. Additionally, we aim to analyze and shed new light on inter- and intrapersonal (mal)adaptive factors in relation to PTG in individuals bereaved by suicide. Ten new studies from 2019 to 2024 were included after searching six databases. Combining these studies with the original review’s 11 studies meant 21 total studies were investigated (N = 4759 participants). A hierarchical meta-analysis examined impacts of demographic, loss-related, intrapersonal, and interpersonal variables on PTG. An extended analysis was also conducted to investigate intrapersonal and interpersonal (mal)adaptive factors’ effects on PTG. The original review’s findings were replicated showing consistent trends; time since loss, social support, and self-disclosure showed significant positive relationships with PTG. The extended analysis found intrapersonal and interpersonal adaptive factors to be significantly positively correlated to PTG while interpersonal maladaptive factors had a significant negative association with PTG. Interestingly, intrapersonal maladaptive factors showed no significant effect on PTG. This review highlights that implementing intrapersonal and interpersonal adaptive factors along with minimizing maladaptive factors could significantly aid PTG development for individuals bereaved by suicide. This is still a new field of study, so further exploration of facilitating and impeding factors of PTG is warranted.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".