Posttraumatic growth in out-of-hospital cardiac arrest survivors: prevalence and associated factors
Bibliographic record
Abstract
Aims While traumatic experiences can be distressing, they may also foster psychological growth, a phenomenon known as post-traumatic growth (PTG). The aims were to determine 1) the prevalence of PTG, and 2) the influence of survivor characteristics during hospitalization on levels of PTG at follow-up in a Danish cohort of out-of-hospital cardiac arrest (OHCA) survivors. Methods A multicenter prospective cohort study including OHCA survivors, exploring soci-odemographic, clinical, and psychosocial characteristics using the Montreal Cognitive Assess-ment (MoCA), the Hospital Anxiety and Depression Scale (HADS), the Impact of Event Scale-Revised (IES-R), and the Crisis Support Scale (CSS) during hospitalization. At three-month follow-up, structured interviews were conducted to assess PTG at personal, relational, and institutional levels. The influence of survivor characteristics on PTG was explored using Pearson’s chi-square tests. Results Overall, 173 survivors were included. At follow-up, 87% of survivors reported hav-ing one or more levels of PTG. The analysis revealed that the absence of cognitive impairment (MoCA ≥26 vs. MoCA <26) was associated with personal growth (p= .02), being younger (<58 years vs. ≥58 years) with relational growth (p= .03) and being female or having symp-toms of depression (HADS ≥8 vs. HADS<8), with institutional growth (p= .02 and p= .04), respectively. Conclusion The OHCA survivors reported high levels of PTG at three-month follow-up. The type of PTG level was influenced by the absence of cognitive impairment, younger age, fe-male sex, and symptoms of depression during hospitalisation. Social support, symptoms of anxiety, and traumatic distress did not significantly influence the level of PTG.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".